diff --git a/examples/huawei_2017/GUIDE.md b/examples/huawei_2017/GUIDE.md
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+# Huawei CodeCraft 2017 CDN Optimization with OpenEvolve
+
+## 问题概述
+
+这是华为 CodeCraft 2017 编程大赛的题目 - 一个视频内容分发网络(CDN)服务器部署优化问题。
+
+**目标**: 最小化总成本 = 服务器部署成本 + 带宽租用成本
+
+**约束条件**:
+- 满足所有消费节点的视频带宽需求
+- 不超过网络链路的带宽容量
+- 不超过服务器的输出能力上限
+
+**问题本质**: 这是一个设施选址(Facility Location)和多商品流(Multi-Commodity Flow)的组合优化问题，属于NP-hard问题。
+
+## OpenEvolve 原理
+
+OpenEvolve 使用**进化算法** + **大语言模型**来优化代码：
+
+1. **MAP-Elites算法**: 维护程序多样性，在特征空间中保留精英解
+2. **Island-Based Evolution**: 多个独立种群并行演化，定期迁移交流
+3. **LLM驱动变异**: 使用Qwen等大模型生成智能代码变异
+4. **级联评估**: 多阶段评估策略，快速过滤低质量解
+5. **进程池并行**: 并行评估多个程序，加速演化
+
+## 文件说明
+
+- `initial_program.py` - 初始基线解决方案（简单贪心策略）
+- `evaluator.py` - 评估器，测试程序在多个案例上的表现
+- `config.qwen.yaml` - Qwen模型配置文件
+- `case_example/` - 测试用例目录
+  - `batch1/0Primary/` - 初级测试用例
+  - `batch1/1Intermediate/` - 中级测试用例
+  - `batch1/2Advanced/` - 高级测试用例
+  - `batch2/`, `batch3/` - 更多测试批次
+
+## 快速开始
+
+### 1. 安装依赖
+
+```bash
+cd /path/to/openevolve
+pip install -e ".[dev]"
+```
+
+### 2. 验证初始程序
+
+```bash
+cd examples/huawei_2017
+
+# 测试单个案例
+python initial_program.py case_example/batch1/0Primary/case0.txt
+
+# 完整评估
+python -c "from evaluator import evaluate; result = evaluate('initial_program.py'); print(result.metrics)"
+```
+
+预期输出:
+```
+{'valid_solutions': 1.0, 'avg_cost': 149920.0, 'combined_score': 0.6267, ...}
+```
+
+### 3. 运行进化优化
+
+**Linux/Mac**:
+```bash
+chmod +x run_evolution.sh
+./run_evolution.sh --iterations 300
+```
+
+**Windows**:
+```batch
+run_evolution.bat --iterations 300
+```
+
+**Python直接调用**:
+```bash
+python ../../openevolve-run.py initial_program.py evaluator.py \
+    --config config.qwen.yaml \
+    --iterations 300
+```
+
+### 4. 查看结果
+
+进化过程中会自动保存：
+
+- `openevolve_output/best/best_program.py` - 最优解
+- `openevolve_output/best/best_program_info.json` - 最优解的性能指标
+- `openevolve_output/checkpoints/checkpoint_N/` - 定期保存的检查点
+- `openevolve_output/logs/` - 详细日志
+
+### 5. 从检查点恢复
+
+如果进化中断，可以从检查点恢复：
+
+```bash
+python ../../openevolve-run.py initial_program.py evaluator.py \
+    --config config.qwen.yaml \
+    --checkpoint openevolve_output/checkpoints/checkpoint_150 \
+    --iterations 50
+```
+
+## 性能指标说明
+
+评估器返回以下指标：
+
+- `valid_solutions` (0.0-1.0): 有效解的比例
+- `avg_cost`: 平均总成本（越低越好）
+- `cost_score` (0.0-1.0): 成本得分（归一化）
+- `time_score` (0.0-1.0): 执行时间得分
+- `combined_score` (0.0-1.0): 综合得分
+  - 公式: `0.5 * valid_rate + 0.4 * cost_score + 0.1 * time_score`
+
+**基线性能** (初始程序):
+- Valid Solutions: 100%
+- Average Cost: ~150,000
+- Combined Score: ~0.63
+
+**优化目标**:
+- 降低平均成本到 100,000 以下
+- 提高 Combined Score 到 0.75+
+
+## 配置参数说明
+
+`config.qwen.yaml` 中的关键参数：
+
+```yaml
+max_iterations: 300          # 进化迭代次数
+checkpoint_interval: 30      # 每30次迭代保存检查点
+
+llm:
+  primary_model: "Qwen/Qwen2.5-Coder-32B-Instruct"  # 使用的模型
+  temperature: 0.8           # 生成的随机性(0-1，越高越随机)
+  max_tokens: 8192           # 单次生成的最大token数
+
+database:
+  num_islands: 4             # 岛屿数量（并行演化的种群数）
+  population_size: 80        # 种群大小
+  elite_selection_ratio: 0.25  # 精英选择比例
+
+evaluator:
+  timeout: 90                # 单个程序评估超时(秒)
+  parallel_evaluations: 3    # 并行评估的程序数量
+```
+
+**调优建议**:
+- 如果想快速实验: `max_iterations: 100`, `population_size: 40`
+- 如果想更好结果: `max_iterations: 500`, `population_size: 120`, `num_islands: 6`
+- 如果API调用受限: 降低 `parallel_evaluations` 和 `num_islands`
+
+## 算法优化方向
+
+OpenEvolve 的 LLM 会尝试探索以下优化策略：
+
+1. **贪心启发式**
+   - 服务器放置策略（靠近高需求区域）
+   - 服务器类型选择（容量vs成本权衡）
+
+2. **局部搜索**
+   - 服务器重定位
+   - 服务器增删
+   - 需求重分配
+
+3. **流量优化**
+   - 更好的路由算法
+   - 带宽成本最小化
+   - 负载均衡
+
+4. **高级技术**
+   - 聚类消费节点
+   - 分层部署策略
+   - 动态规划组件
+   - 启发式剪枝
+
+## 常见问题
+
+### Q: 进化过程很慢怎么办？
+
+A: 可以调整以下参数加速：
+- 减少 `max_iterations`
+- 减少测试用例数量（在evaluator.py中调整`load_test_cases()[:5]`）
+- 增加 `parallel_evaluations`（如果有更多CPU核心）
+- 使用更快的LLM模型
+
+### Q: 所有解都无效怎么办？
+
+A: 检查：
+1. 初始程序能否正常运行
+2. 评估器是否配置正确
+3. 约束条件是否理解正确
+4. LLM生成的代码是否有语法错误（查看日志）
+
+### Q: 成本没有下降怎么办？
+
+A: 尝试：
+1. 增加 `temperature` 提高探索性
+2. 增加 `population_size` 和 `num_islands`
+3. 调整 `elite_selection_ratio` 和 `exploitation_ratio`
+4. 修改 prompt 提供更多优化提示
+
+### Q: 如何使用其他模型？
+
+A: 修改 `config.qwen.yaml` 中的 `llm.primary_model`：
+
+```yaml
+# 使用 OpenAI GPT-4
+llm:
+  primary_model: "gpt-4"
+  api_key: "your-openai-api-key"
+  api_base: "https://api.openai.com/v1"
+
+# 使用本地模型（需要兼容OpenAI API）
+llm:
+  primary_model: "local-model-name"
+  api_base: "http://localhost:8000/v1"
+```
+
+## 进阶使用
+
+### 可视化演化树
+
+```bash
+python ../../scripts/visualizer.py \
+    --path openevolve_output/checkpoints/checkpoint_300/
+```
+
+### 分析最佳程序
+
+```python
+import json
+
+# 读取最佳程序信息
+with open('openevolve_output/best/best_program_info.json') as f:
+    info = json.load(f)
+    print(f"Generation: {info['generation']}")
+    print(f"Found at iteration: {info['iteration']}")
+    print(f"Metrics: {info['metrics']}")
+
+# 读取并测试最佳程序
+with open('openevolve_output/best/best_program.py') as f:
+    best_code = f.read()
+    print(best_code)
+```
+
+### 批量测试
+
+```python
+from evaluator import evaluate
+import glob
+
+for checkpoint_dir in sorted(glob.glob('openevolve_output/checkpoints/checkpoint_*')):
+    program_path = f'{checkpoint_dir}/best_program.py'
+    if os.path.exists(program_path):
+        result = evaluate(program_path)
+        print(f"{checkpoint_dir}: {result.metrics['combined_score']:.4f}")
+```
+
+## 参考资料
+
+- [OpenEvolve 文档](../../CLAUDE.md)
+- [华为 CodeCraft 2017 题目](README.md)
+- [MAP-Elites 算法论文](https://arxiv.org/abs/1504.04909)
+- [AlphaEvolve 论文](https://deepmind.google/discover/blog/alphaevolve/)
+
+## 许可证
+
+本示例遵循 OpenEvolve 项目的许可证。
diff --git a/examples/huawei_2017/QUICK_START.md b/examples/huawei_2017/QUICK_START.md
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+# 快速开始 - 华为2017 CDN优化
+
+## 5分钟快速运行
+
+### 1. 确认环境
+```bash
+cd openevolve/examples/huawei_2017
+python -c "from evaluator import evaluate; print('OK')"
+```
+
+### 2. 测试初始程序
+```bash
+python initial_program.py case_example/batch1/0Primary/case0.txt
+```
+
+应该看到成本输出和路径方案。
+
+### 3. 验证评估
+```bash
+python -c "from evaluator import evaluate; r = evaluate('initial_program.py'); print(f'Score: {r.metrics[\"combined_score\"]:.4f}')"
+```
+
+应该看到 Score: ~0.6267
+
+### 4. 运行进化（短测试）
+```bash
+python ..\..\openevolve-run.py initial_program.py evaluator.py --config config.qwen.yaml --iterations 100 --checkpoint .\openevolve_output\checkpoints\checkpoint_975
+```
+
+### 5. 查看结果
+```bash
+cat openevolve_output/best/best_program_info.json
+```
+
+## 完整运行（300次迭代）
+
+**Windows**:
+```batch
+run_evolution.bat
+```
+
+**Linux/Mac**:
+```bash
+chmod +x run_evolution.sh
+./run_evolution.sh
+```
+
+预计时间: 3-5小时（取决于API速度）
+
+## 关键文件
+
+- ✅ `initial_program.py` - 基线程序（成本~150,000）
+- ✅ `evaluator.py` - 评估器（测试5个案例）
+- ✅ `config.qwen.yaml` - Qwen模型配置
+- 📁 `case_example/` - 测试用例（80个）
+- 📊 `openevolve_output/` - 进化结果（运行后生成）
+
+## 预期效果
+
+| 指标 | 初始值 | 目标 |
+|------|--------|------|
+| 有效率 | 100% | 100% |
+| 平均成本 | 150,000 | < 100,000 |
+| 综合得分 | 0.63 | > 0.75 |
+
+## 常见问题
+
+### API Key配置
+
+编辑 `config.qwen.yaml`:
+```yaml
+llm:
+  api_key: "your-api-key-here"
+  api_base: "https://your-api-endpoint/"
+```
+
+### 加速测试
+
+减少迭代次数和测试用例：
+```bash
+python ../../openevolve-run.py initial_program.py evaluator.py \
+    --config config.qwen.yaml --iterations 50
+```
+
+在 `evaluator.py` 中修改:
+```python
+test_cases = load_test_cases(case_dir)[:2]  # 只测试2个案例
+```
+
+### 查看详细日志
+
+```bash
+tail -f openevolve_output/logs/openevolve_*.log
+```
+
+## 下一步
+
+详细文档请查看:
+- `GUIDE.md` - 完整使用指南
+- `README.md` - 问题描述
+- `../../CLAUDE.md` - OpenEvolve架构
+
+祝优化顺利！ 🚀
diff --git a/examples/huawei_2017/README.md b/examples/huawei_2017/README.md
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+# HWCC2017
+
+Codes for Huawei CodeCraft 2017.
+
+---
+
+## 复赛赛题  (以下描述部分主要是相对初赛赛题变化点，其他描述和条件均一致)
+
+### 通用性描述变化点：
+
+视频内容服务器：视频内容服务器存放视频内容（如：电影影片、电视剧等），视频内容服务器的视频数据流可以经由网络节点与链路构成的网络路径流向消费节点，一台视频内容服务器可以服务多个消费节点，而一个消费节点也可以同时从多台视频内容服务器获取视频流。购买一台视频内容服务器需要硬件成本，每台视频内容服务器的输出能力存在上限（例如150Gbps/台），并分为若干档次（如0，1，2三个档次），不同档次的输出能力上限与硬件成本均不同（例如档次1的服务器输出能力为100Gbps/台，硬件成本为100千元/台，档次2的服务器输出能力为150Gbps，硬件成本为200千元/台）。此外对于某个网络节点，部署一台视频内容服务器需要支付额外的部署成本（例如某个网络节点的部署成本为50千元/台），不同网络节点的部署成本可能不同。因此部署一台视频内容服务器到某网络节点的成本为该服务器硬件成本与该节点部署成本之和。
+
+补充说明变化点：
+
+1. 网络节点数量不超过10000个，每个节点的链路数量不超过10000条，消费节点的数量不超过10000个。
+
+2. 链路总带宽与网络租用费为[0, 100]的整数，视频内容服务器档次数不超过10个。视频内容服务器输出能力，网络节点的部署成本，消费节点的视频带宽消耗需求均为[0,10000]的整数。
+
+3. 以一台视频内容服务器对应网络节点为起点的所有网络路径上的占用带宽总和不得超过该台服务器的输出能力上限。
+
+### 程序输入与输出变化点：
+
+#### 输入文件格式
+
+程序输入为一个以空格分隔的文本文件，文件每行以换行符（‘\n’或者‘\r\n’）为结尾。 
+
+文件格式为：
+
+网络节点数量 网络链路数量 消费节点数量
+
+（空行）
+
+服务器硬件档次ID 输出能力 硬件成本
+
+……（如上视频内容服务器信息若干行）
+
+（空行）
+
+网络节点ID 部署成本
+
+……（如上网络节点部署成本信息若干行）
+
+（空行）
+
+链路起始节点ID 链路终止节点ID 总带宽大小 单位网络租用费
+
+…………….（如上链路信息若干行）
+
+（空行）
+
+消费节点ID 相连网络节点ID 视频带宽消耗需求
+
+…………….（如上终端用户信息若干行）
+
+（文件结束）
+
+说明：
+
+1.网络节点ID与消费节点ID均为以0为起始的连续整数。
+
+2.文本中出现的所有数值均为大于等于0的整数，数值上限为1000000。
+
+输入文件示例（参照第一节中的用例）：
+
+28 45 12 // 注：28个网络节点，45条链路，12个消费节点
+
+
+
+0 70 50// 注：服务器硬件档次ID为0，最大输出能力为70，硬件成本为50 
+
+1 100 100
+
+2 150 200
+
+
+
+0 50 // 注：网络节点ID为0，部署成本为50
+
+1 50
+
+2 50
+
+3 120
+
+4 80
+
+5 120
+
+
+
+(以下省略若干行网络节点信息)
+
+0 16 8 2 // 注：链路起始网络节点ID为0，链路终止网络节点ID为16 ，总带宽为8，单位网络租用费为2 
+
+0 26 13 2
+
+0 9 14 2
+
+0 8 36 2
+
+(以下省略若干行链路信息)
+
+
+
+0 8 40 // 注：消费节点ID为0，相连网络节点ID为8，视频带宽消耗需求为40 
+
+1 11 13
+
+2 22 28
+
+3 3 45
+
+4 17 11
+
+5 19 26
+
+6 16 15
+
+7 13 13
+
+8 5 18
+
+9 25 15
+
+10 7 10
+
+11 24 23
+
+#### 输出文件格式
+
+程序输出为一个以空格分隔的文本文件，文件每行以换行符（‘\n’或者‘\r\n’）为结尾。
+
+文件格式为：
+
+网络路径数量
+
+（空行）
+
+网络节点ID-01 网络节点ID-02 …… 网络节点ID-n 消费节点ID 占用带宽大小 服务器硬件档次ID
+
+…………….（如上网络路径信息若干行，每条网络路径由若干网络节点构成，路径的起始节点ID-01表示该节点部署了视频内容服务器，终止节点为某个消费节点）
+
+（文件结束）
+
+说明：
+
+1. 网络路径数量不得超过300000条。
+
+2. 单条路径的节点数量不得超过10000个。
+
+3. 不同网络路径可按任意先后顺序输出。
+
+4. 网络节点ID与消费节点ID的数值必须与输入文件相符合，如果ID数值不存在于输入文件中，则将被视为无效结果。
+
+5. 文本文件中出现的所有数值必须为大于等于0的整数，数值大小不得超过1000000。
+
+补充说明：
+
+4月9日-4月22日系统会提供练习用例用于判题与排名，北京时间4月23日13:30提供正式复赛用例。
+
+
+
+## 初赛赛题
+
+
+### 比赛问题定义
+
+背景：大视频解决方案中，视频业务体验非常关键，视频内容如何有效传送到最终消费者是决定视频体验好坏的核心环节。
+
+#### 本次赛题基本描述
+
+在给定结构的G省电信网络中，为了视频内容快速低成本的传送到每个住户小区，需要在这个给定网络结构中选择一些网络节点附近放置视频内容存储服务器。需要解决的问题是：在满足所有的住户小区视频播放需求的基本前提下，如何选择视频内容存储服务器放置位置，使得成本最小。
+
+#### 本次赛题通用性描述
+
+网络结构模型：给定一个由若干网络节点（例如路由器、交换机）构成的网络结构无向图，每个节点至少与另外一个节点通过网络链路相连（网络链路特指两个网络节点之间直接相连的网络通路，中间没有其他网络节点，相当于无向图中的一条边），一个节点可以将收到的数据通过网络链路传输给相连的另一个节点。每条链路的网络总带宽不同（例如某条链路的总带宽为10Gbps）。而每条链路承载的视频传输需要按照占用带宽的多少收取对应网络租用费，每条链路的单位租用费均不同（例如某条链路的租用费为1,000元/Gbps，即1K/Gbps）。某条链路上被占用的带宽总和不得超过该链路的总带宽。
+
+消费节点：给定的网络结构中有部分网络节点直接连接到小区住户的网络，每个小区住户网络在这个给定的网络结构图中呈现为一个消费节点，不同消费节点的视频带宽消耗需求不同。
+
+视频内容服务器：视频内容服务器存放视频内容（如：电影影片、电视剧等），视频内容服务器的视频数据流可以经由网络节点与链路构成的网络路径流向消费节点，视频内容服务器的输出能力没有上限，可以服务多个消费节点，一个消费节点也可以同时从多台视频内容服务器获取视频流。部署一台视频内容服务器需要费用成本（例如300,000元/台，即300K/台），所有服务器的成本均相同。
+
+比赛程序内容：请你设计一个程序寻找最优的视频内容服务器部署方案：从网络结构模型中选择一部分网络节点，在其上/附近一对一的部署视频内容服务器，视频内容服务器与对应的这个节点直连，与对应的这个网络节点之间的通信没有带宽限制、也没有通信成本。提供的部署方案需要使得视频流从视频内容服务器经过一些网络节点和链路到达消费节点，并满足所有消费节点的视频带宽消耗需求。
+
+在满足所有消费节点视频带宽消耗需求的前提下，使得耗费的总成本（视频内容服务器部署成本+带宽租用成本）最低。部署方案不仅需要包括部署视频内容服务器的节点位置，而且还要包括每个消费节点与所有视频内容服务器之间的网络路径以及路径上占用的带宽。
+
+#### 比赛胜负规则
+
+若给定的网络拓扑模型不存在满足条件的方案，则输出无解，程序运行时间越短者胜出。若存在满足条件的方案，则成本越低者胜出。若两个方案的成本相同，则程序运行时间越短者胜出。
+
+#### 补充说明：
+
+1. 两个网络节点之间最多仅存在一条链路，链路上下行方向的网络总带宽相互独立，并且上下行方向的总带宽与网络租用费相同。例如对于网络节点A与B之间的链路，该条链路上的总带宽为10Gbps，单位租用费为1K/Gbps，则表示A->B、B->A两个方向上的网络总带宽分别为10Gbps，并且租用费均为1K/Gbps。如果某条数据流在该链路A->B方向的占用带宽为3Gbps，那么该数据流在该链路的租用费为3K，并且该链路A->B方向的剩余可用带宽为7Gbps。而B->A方向的剩余可用带宽不受该数据流的影响，仍为10Gbps。
+
+2. 每个网络节点最多仅能连接一个消费节点，每个消费节点仅能连接一个网络节点。消费节点与连接的网络节点之间的链路总带宽无限大，并且网络租用费为零。
+
+3. 网络节点数量不超过1000个，每个节点的链路数量不超过20条，消费节点的数量不超过500个。
+
+4. 链路总带宽与网络租用费为[0, 100]的整数，视频内容服务器部署成本与消费节点的视频带宽消耗需求为[0,5000]的整数。
+
+5. 部署方案中，网络路径上的占用带宽必须为整数。
+
+6. “满足消费节点的带宽消耗需求”是指输出给消费节点的带宽总和不得小于该消费节点的视频带宽消耗需求。
+
+7. 每个网络节点上最多仅可部署一台视频内容服务器。
+
+#### 比赛用例示例：
+
+![](detail_01.png)
+
+上图为G省网络拓扑图，黑色圆圈为网络节点，红色圆圈为消费节点，圆圈内的数字为节点编号。节点之间的连线为网络链路。链路上的标记(x, y)中，x表示链路总带宽（单位为Gbps），y表示每Gbps的网络租用费。消费节点相连链路上的数字为消费节点的带宽消耗需求（单位为Gbps）。
+
+现在假设需要在该网络上部署视频内容服务器，满足所有消费节点的需求。一个成本较低的方案如下图所示，其中绿色圆圈表示已部署的视频内容服务器，通往不同消费节点的网络路径用不同颜色标识，并附带了占用带宽的大小：
+
+![](detail_02.png)
+
+但该方案的成本不一定是最低的。因此现在需要参赛者提供的程序能够针对不同的比赛用例，给出成本最低的部署方案。
+
+
+### 程序输入与输出
+
+#### 输入文件格式
+
+程序输入为一个以空格分隔的文本文件，文件每行以换行符（ASCII’\n’即0x0a）为结尾。 
+
+
+
+文件格式为：
+
+网络节点数量 网络链路数量 消费节点数量
+
+（空行）
+
+视频内容服务器部署成本
+
+（空行）
+
+链路起始节点ID 链路终止节点ID 总带宽大小 单位网络租用费
+
+…………….（如上链路信息若干行）
+
+（空行）
+
+消费节点ID 相连网络节点ID 视频带宽消耗需求
+
+…………….（如上终端用户信息若干行）
+
+（文件结束）
+
+说明：
+
+1. 网络节点ID与消费节点ID均为以0为起始的整数。
+
+2. 文本中出现的所有数值均为大于等于0的整数，数值上限为100000。
+
+输入文件示例（参照第一节中的用例）：
+
+28 45 12 // 注：28个网络节点，45条链路，12个消费节点
+
+
+
+100 // 注：服务器部署成本为100
+
+
+
+0 16 8 2 // 注：链路起始节点为0，链路终止节点为16 ，总带宽为8，单位网络租用费为2
+
+0 26 13 2
+
+0 9 14 2
+
+0 8 36 2
+
+(以下省略若干行网络节点信息)
+
+0 8 40 // 注：消费节点0，相连网络节点ID为8，视频带宽消耗需求为40 
+
+1 11 13
+
+2 22 28
+
+3 3 45
+
+4 17 11
+
+5 19 26
+
+6 16 15
+
+7 13 13
+
+8 5 18
+
+9 25 15
+
+10 7 10
+
+11 24 23
+
+
+
+#### 输出文件格式
+
+程序输出为一个以空格分隔的文本文件，文件每行以换行符（ASCII’\n’即0x0a）为结尾。
+
+文件格式为：
+
+如果不存在满足条件的方案，则输出一行
+
+NA
+
+（文件结束）
+
+如果存在满足条件的方案，则按如下格式输出：
+
+网络路径数量
+
+（空行）
+
+网络节点ID-01 网络节点ID-02 …… 网络节点ID-n 消费节点ID 占用带宽大小
+
+…………….（如上网络路径信息若干行，每条网络路径由若干网络节点构成，路径的起始节点ID-01表示该节点部署了视频内容服务器，终止节点为某个消费节点）
+
+（文件结束）
+
+说明：
+
+1. 网络路径数量不得超过50000条。
+
+2. 单条路径的节点数量不得超过1000个。
+
+3. 不同网络路径可按任意先后顺序输出。
+
+4. 网络节点ID与消费节点ID的数值必须与输入文件相符合，如果ID数值不存在于输入文件中，则将被视为无效结果。
+
+5. 文本文件中出现的所有数值必须为大于等于0的整数，数值大小不得超过100000。
+
+输出文件示例（参照第一节中的用例部署方案）：
+
+19 // 注：共输出19条网络路径
+
+
+
+0 9 11 1 13 // 注：起始网络节点ID为0，经由ID为9、11等网络节点到达消费节点1，占用带宽为13
+
+0 7 10 10
+
+0 8 0 36
+
+0 6 5 8 13
+
+（以下省略网络路径若干行）
+
+
+### 单个用例的评分机制
+
+#### 有解用例的排名机制
+
+按下面流程对参赛者结果进行排名：
+
+Step1： 对于提交的结果，进行合法性检验(详见题目描述)；
+
+Step2： 单个用例的程序运行时间不得超过90s；
+
+若不满足上述的结果则本用例得分为0；
+
+Step3： 计算部署方案的成本，成本越小，排名越优；
+
+Step4： 在成本相同的结果里，用程序运行时间进行排名，时间越短，排名越优。
+
+#### 无解用例的排名机制
+
+按下列流程对参赛者结果进行排名：
+
+Step1： 对于提交的结果，验证是否识别出该用例无解，若无法识别或者算法运行时间超90s，则本用例得分为0；
+
+Step2： 用程序的运行时间进行排名，时间越短，排名越优。
+
+单个用例的评分标准如下：
+
+根据上面排名流程得到的排名，使用标准分计分(排名第一的提交者为100分)。
+
+若所有人均未得到正确结果，则所有人均得分为0。
+
+
+### 最终得分机制
+
+比赛平台会使用N个测试用例判题，该N个测试用例分为初级、中级、高级三个等级，参赛者对于每个测试用例都会得到一个百分制分数，使用加权平均分(初级权重为0.2，中级权重为0.3，高级权重为0.5)作为该参赛者的最终得分。
+
+补充说明：前期系统会提供练习用例用于判题与排名，4月1日开始提供正式用例。截止初赛结束前，系统将在用例上自动运行选手最后一次提交的代码并进行评分，所得分数为初赛最终成绩。 在比赛初期，比赛平台只提供初级、中级的练习用例，故此时满分为50分，在比赛后期，才会提供高级练习用例（具体时间会在网站公告通知），此时满分才为100。
+
+
+    本次大赛禁止使用第三方开源代码，一旦发现将取消比赛成绩。
+
+
+## 运行环境
+
+CPU：Intel(R) Xeon(R) CPU E5-2680 V4 @ 2.40GHz
+
+内存：2G
+
+内核：单核
+
+编译器：gcc 4.8.4；java 1.7.0_95；
+
+操作系统：linux Ubuntu 14.04.4 LTS 64位，内核版本 Linux version 3.13.0-108-generic
+
+SDK：为方便选手做题，分别提供c++(兼容c）和Java SDK包供参考（见赛题下载包），详细描述信息请见SDK目录下的readme.txt。
\ No newline at end of file
diff --git a/examples/huawei_2017/SCORING_GUIDE.md b/examples/huawei_2017/SCORING_GUIDE.md
new file mode 100644
index 00000000..1fb33a3d
--- /dev/null
+++ b/examples/huawei_2017/SCORING_GUIDE.md
@@ -0,0 +1,173 @@
+# Huawei 2017 CDN - Scoring System Guide
+
+## Overview
+
+This document explains the scoring system used to evaluate solutions for the Huawei CodeCraft 2017 CDN optimization problem.
+
+## Scoring Formula
+
+```
+combined_score = 0.40 × success_rate + 0.35 × cost_score + 0.25 × time_score
+```
+
+### Component Weights
+
+| Component | Weight | Description |
+|-----------|--------|-------------|
+| **Success Rate** | 40% | Percentage of test cases solved correctly |
+| **Cost Score** | 35% | How low the total cost is (primary competition metric) |
+| **Time Score** | 25% | How fast the solution executes (CRITICAL!) |
+
+## Detailed Score Calculations
+
+### 1. Success Rate
+```python
+success_rate = valid_solutions / total_test_cases
+```
+- Range: [0.0, 1.0]
+- Each failed test case reduces this proportionally
+
+### 2. Cost Score
+```python
+cost_score = 10000.0 / (avg_cost + 1.0)
+```
+- Normalized using a reference cost of 10,000
+- Lower costs yield higher scores
+- This is the PRIMARY competition ranking metric
+
+### 3. Time Score (NEW - Exponential Decay)
+```python
+time_score = exp(-avg_time / 10.0)
+
+# Bonus for very fast solutions (< 1s)
+if avg_time < 1.0:
+    time_score = min(1.0, time_score × 1.2)
+```
+
+**Time Score Table:**
+
+| Avg Time | Time Score | Impact (×0.25) | Assessment |
+|----------|------------|----------------|------------|
+| 0.5s     | 1.0000     | 0.2500         | Excellent  |
+| 1.0s     | 0.9048     | 0.2262         | Excellent  |
+| 2.0s     | 0.8187     | 0.2047         | Excellent  |
+| 3.0s     | 0.7408     | 0.1852         | Excellent  |
+| **5.0s** | **0.6065** | **0.1516**     | **Good**   |
+| 7.0s     | 0.4966     | 0.1241         | Good       |
+| **10.0s**| **0.3679** | **0.0920**     | Acceptable |
+| 15.0s    | 0.2231     | 0.0558         | Acceptable |
+| **20.0s**| **0.1353** | **0.0338**     | Poor       |
+| 25.0s    | 0.0821     | 0.0205         | Poor       |
+| **30.0s**| **0.0498** | **0.0124**     | Very Poor  |
+
+## Key Insights
+
+### Time Performance Impact
+
+1. **Time weight is 25%** - This is NOT negligible!
+2. **Target: < 5 seconds per test case** for good scores
+3. **Hard limit: 30 seconds** - Solutions timing out get 0 score
+
+### Performance Degradation Examples
+
+Assuming 100% success rate and perfect cost:
+
+```
+Time  1s → combined_score = 0.9762
+Time  5s → combined_score = 0.9016  (-0.075 vs 1s)
+Time 10s → combined_score = 0.8420  (-0.060 vs 5s)
+Time 20s → combined_score = 0.7838  (-0.058 vs 10s)
+Time 30s → combined_score = 0.7624  (-0.021 vs 20s)
+```
+
+**Going from 5s to 10s costs ~6% of total score!**
+
+## Algorithm Optimization Priorities
+
+### 1. First: Ensure Correctness (40%)
+- All constraints must be satisfied
+- Invalid solutions get 0 score
+
+### 2. Then: Minimize Cost (35%)
+- This is the primary competition metric
+- Focus on:
+  - Better server placement
+  - Optimal routing
+  - Server type selection
+
+### 3. Always: Keep It Fast (25%)
+- **Don't ignore execution time!**
+- Complexity matters:
+  - O(N²) is acceptable
+  - O(N³) may timeout on large cases
+- Use efficient data structures
+- Avoid redundant computations
+
+## Evolution Strategy Recommendations
+
+### Balance Cost vs. Time Trade-offs
+
+1. **Quick wins first**: Simple, fast heuristics
+2. **Iterative refinement**: Add complexity only if time allows
+3. **Profiling**: Identify bottlenecks
+4. **Early termination**: Stop optimization when improvement is marginal
+
+### Example Trade-off Analysis
+
+```
+Option A: Greedy algorithm
+  - Cost: 8,000
+  - Time: 2s
+  - Combined: 0.40×1.0 + 0.35×1.25 + 0.25×0.82 = 1.04
+
+Option B: Complex optimization
+  - Cost: 7,500
+  - Time: 15s
+  - Combined: 0.40×1.0 + 0.35×1.33 + 0.25×0.22 = 0.92
+
+Option A wins! Faster execution compensates for slightly higher cost.
+```
+
+## Artifacts Reported
+
+The evaluator now reports:
+
+```json
+{
+  "metrics": {
+    "valid_solutions": 1.0,
+    "avg_cost": 7234.5,
+    "avg_time": 4.23,
+    "cost_score": 1.382,
+    "time_score": 0.655,
+    "combined_score": 0.928
+  },
+  "artifacts": {
+    "num_test_cases": 5,
+    "num_valid": 5,
+    "avg_time_per_case": "4.23s",
+    "total_time": "21.15s",
+    "time_breakdown": {
+      "fastest": "3.12s",
+      "slowest": "5.87s"
+    },
+    "scoring_weights": {
+      "success_rate": "40%",
+      "cost": "35%",
+      "time": "25%"
+    }
+  }
+}
+```
+
+## Summary
+
+- ✅ **Time is critical**: 25% weight, exponential penalty
+- ✅ **Target performance**: < 5 seconds per test case
+- ✅ **Cost still matters most**: 35% weight
+- ✅ **Must be correct first**: 40% for success rate
+- ✅ **Balance is key**: Don't over-optimize one metric at the expense of others
+
+---
+
+**Remember**: In the actual competition, cost is the PRIMARY ranking metric among solutions with the same success rate. Our scoring system reflects this by balancing all three factors appropriately for evolution.
diff --git a/examples/huawei_2017/case_example/batch1/0Primary/case0.txt b/examples/huawei_2017/case_example/batch1/0Primary/case0.txt
new file mode 100644
index 00000000..6540cba4
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/0Primary/case0.txt
@@ -0,0 +1,609 @@
+160 374 64
+
+0 30 200
+1 60 400
+2 120 1000
+3 180 1600
+4 240 2400
+5 300 3000
+
+0 1500
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diff --git a/examples/huawei_2017/case_example/batch1/0Primary/case1.txt b/examples/huawei_2017/case_example/batch1/0Primary/case1.txt
new file mode 100644
index 00000000..8e12d062
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/0Primary/case1.txt
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diff --git a/examples/huawei_2017/case_example/batch1/0Primary/case2.txt b/examples/huawei_2017/case_example/batch1/0Primary/case2.txt
new file mode 100644
index 00000000..bce4abf7
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/0Primary/case2.txt
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diff --git a/examples/huawei_2017/case_example/batch1/0Primary/case3.txt b/examples/huawei_2017/case_example/batch1/0Primary/case3.txt
new file mode 100644
index 00000000..8ebafbf0
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/0Primary/case3.txt
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diff --git a/examples/huawei_2017/case_example/batch1/0Primary/case4.txt b/examples/huawei_2017/case_example/batch1/0Primary/case4.txt
new file mode 100644
index 00000000..77e3d739
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/0Primary/case4.txt
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diff --git a/examples/huawei_2017/case_example/batch1/0Primary/case5.txt b/examples/huawei_2017/case_example/batch1/0Primary/case5.txt
new file mode 100644
index 00000000..d3afd0c9
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/0Primary/case5.txt
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diff --git a/examples/huawei_2017/case_example/batch1/0Primary/case6.txt b/examples/huawei_2017/case_example/batch1/0Primary/case6.txt
new file mode 100644
index 00000000..25c31569
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/0Primary/case6.txt
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diff --git a/examples/huawei_2017/case_example/batch1/0Primary/case7.txt b/examples/huawei_2017/case_example/batch1/0Primary/case7.txt
new file mode 100644
index 00000000..1a83126f
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/0Primary/case7.txt
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diff --git a/examples/huawei_2017/case_example/batch1/0Primary/case8.txt b/examples/huawei_2017/case_example/batch1/0Primary/case8.txt
new file mode 100644
index 00000000..80db3fcf
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/0Primary/case8.txt
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diff --git a/examples/huawei_2017/case_example/batch1/0Primary/case9.txt b/examples/huawei_2017/case_example/batch1/0Primary/case9.txt
new file mode 100644
index 00000000..b5603cec
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/0Primary/case9.txt
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diff --git a/examples/huawei_2017/case_example/batch1/1Intermediate/case0.txt b/examples/huawei_2017/case_example/batch1/1Intermediate/case0.txt
new file mode 100644
index 00000000..f2038e25
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/1Intermediate/case0.txt
@@ -0,0 +1,1225 @@
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diff --git a/examples/huawei_2017/case_example/batch1/1Intermediate/case1.txt b/examples/huawei_2017/case_example/batch1/1Intermediate/case1.txt
new file mode 100644
index 00000000..e3de7d6b
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/1Intermediate/case1.txt
@@ -0,0 +1,1241 @@
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diff --git a/examples/huawei_2017/case_example/batch1/1Intermediate/case2.txt b/examples/huawei_2017/case_example/batch1/1Intermediate/case2.txt
new file mode 100644
index 00000000..252dcc92
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/1Intermediate/case2.txt
@@ -0,0 +1,1236 @@
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diff --git a/examples/huawei_2017/case_example/batch1/1Intermediate/case3.txt b/examples/huawei_2017/case_example/batch1/1Intermediate/case3.txt
new file mode 100644
index 00000000..15b29c23
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/1Intermediate/case3.txt
@@ -0,0 +1,1235 @@
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diff --git a/examples/huawei_2017/case_example/batch1/1Intermediate/case4.txt b/examples/huawei_2017/case_example/batch1/1Intermediate/case4.txt
new file mode 100644
index 00000000..e09a957a
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/1Intermediate/case4.txt
@@ -0,0 +1,1246 @@
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diff --git a/examples/huawei_2017/case_example/batch1/1Intermediate/case5.txt b/examples/huawei_2017/case_example/batch1/1Intermediate/case5.txt
new file mode 100644
index 00000000..7a32dd03
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/1Intermediate/case5.txt
@@ -0,0 +1,1237 @@
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diff --git a/examples/huawei_2017/case_example/batch1/1Intermediate/case6.txt b/examples/huawei_2017/case_example/batch1/1Intermediate/case6.txt
new file mode 100644
index 00000000..57b9f51f
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/1Intermediate/case6.txt
@@ -0,0 +1,1237 @@
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diff --git a/examples/huawei_2017/case_example/batch1/1Intermediate/case7.txt b/examples/huawei_2017/case_example/batch1/1Intermediate/case7.txt
new file mode 100644
index 00000000..70335784
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/1Intermediate/case7.txt
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diff --git a/examples/huawei_2017/case_example/batch1/1Intermediate/case8.txt b/examples/huawei_2017/case_example/batch1/1Intermediate/case8.txt
new file mode 100644
index 00000000..494ea265
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/1Intermediate/case8.txt
@@ -0,0 +1,1257 @@
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diff --git a/examples/huawei_2017/case_example/batch1/1Intermediate/case9.txt b/examples/huawei_2017/case_example/batch1/1Intermediate/case9.txt
new file mode 100644
index 00000000..b9698190
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/1Intermediate/case9.txt
@@ -0,0 +1,1253 @@
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diff --git a/examples/huawei_2017/case_example/batch1/2Advanced/case0.txt b/examples/huawei_2017/case_example/batch1/2Advanced/case0.txt
new file mode 100644
index 00000000..9307fc06
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/2Advanced/case0.txt
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diff --git a/examples/huawei_2017/case_example/batch1/2Advanced/case1.txt b/examples/huawei_2017/case_example/batch1/2Advanced/case1.txt
new file mode 100644
index 00000000..4bb1b931
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/2Advanced/case1.txt
@@ -0,0 +1,3893 @@
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diff --git a/examples/huawei_2017/case_example/batch1/2Advanced/case2.txt b/examples/huawei_2017/case_example/batch1/2Advanced/case2.txt
new file mode 100644
index 00000000..f8b32280
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/2Advanced/case2.txt
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diff --git a/examples/huawei_2017/case_example/batch1/2Advanced/case3.txt b/examples/huawei_2017/case_example/batch1/2Advanced/case3.txt
new file mode 100644
index 00000000..fcfb7b63
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/2Advanced/case3.txt
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diff --git a/examples/huawei_2017/case_example/batch1/2Advanced/case4.txt b/examples/huawei_2017/case_example/batch1/2Advanced/case4.txt
new file mode 100644
index 00000000..c8d571c3
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/2Advanced/case4.txt
@@ -0,0 +1,3909 @@
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diff --git a/examples/huawei_2017/case_example/batch1/2Advanced/case5.txt b/examples/huawei_2017/case_example/batch1/2Advanced/case5.txt
new file mode 100644
index 00000000..a1338946
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/2Advanced/case5.txt
@@ -0,0 +1,3889 @@
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diff --git a/examples/huawei_2017/case_example/batch1/2Advanced/case6.txt b/examples/huawei_2017/case_example/batch1/2Advanced/case6.txt
new file mode 100644
index 00000000..d2a94430
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/2Advanced/case6.txt
@@ -0,0 +1,3907 @@
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diff --git a/examples/huawei_2017/case_example/batch1/2Advanced/case7.txt b/examples/huawei_2017/case_example/batch1/2Advanced/case7.txt
new file mode 100644
index 00000000..d0622c7d
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/2Advanced/case7.txt
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diff --git a/examples/huawei_2017/case_example/batch1/2Advanced/case8.txt b/examples/huawei_2017/case_example/batch1/2Advanced/case8.txt
new file mode 100644
index 00000000..d918cfaa
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/2Advanced/case8.txt
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diff --git a/examples/huawei_2017/case_example/batch1/2Advanced/case9.txt b/examples/huawei_2017/case_example/batch1/2Advanced/case9.txt
new file mode 100644
index 00000000..6250b76a
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch1/2Advanced/case9.txt
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diff --git a/examples/huawei_2017/case_example/batch2/1Intermediate/case0.txt b/examples/huawei_2017/case_example/batch2/1Intermediate/case0.txt
new file mode 100644
index 00000000..51c7bf1b
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/1Intermediate/case0.txt
@@ -0,0 +1,2385 @@
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diff --git a/examples/huawei_2017/case_example/batch2/1Intermediate/case1.txt b/examples/huawei_2017/case_example/batch2/1Intermediate/case1.txt
new file mode 100644
index 00000000..120bfbaa
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/1Intermediate/case1.txt
@@ -0,0 +1,2383 @@
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diff --git a/examples/huawei_2017/case_example/batch2/1Intermediate/case2.txt b/examples/huawei_2017/case_example/batch2/1Intermediate/case2.txt
new file mode 100644
index 00000000..efe4b964
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/1Intermediate/case2.txt
@@ -0,0 +1,2407 @@
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diff --git a/examples/huawei_2017/case_example/batch2/1Intermediate/case3.txt b/examples/huawei_2017/case_example/batch2/1Intermediate/case3.txt
new file mode 100644
index 00000000..da8d2507
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/1Intermediate/case3.txt
@@ -0,0 +1,2403 @@
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diff --git a/examples/huawei_2017/case_example/batch2/1Intermediate/case4.txt b/examples/huawei_2017/case_example/batch2/1Intermediate/case4.txt
new file mode 100644
index 00000000..cc531461
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/1Intermediate/case4.txt
@@ -0,0 +1,2375 @@
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diff --git a/examples/huawei_2017/case_example/batch2/1Intermediate/case5.txt b/examples/huawei_2017/case_example/batch2/1Intermediate/case5.txt
new file mode 100644
index 00000000..c8be2ddb
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/1Intermediate/case5.txt
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diff --git a/examples/huawei_2017/case_example/batch2/1Intermediate/case6.txt b/examples/huawei_2017/case_example/batch2/1Intermediate/case6.txt
new file mode 100644
index 00000000..0b344209
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/1Intermediate/case6.txt
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diff --git a/examples/huawei_2017/case_example/batch2/1Intermediate/case7.txt b/examples/huawei_2017/case_example/batch2/1Intermediate/case7.txt
new file mode 100644
index 00000000..eb9c9f38
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/1Intermediate/case7.txt
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diff --git a/examples/huawei_2017/case_example/batch2/1Intermediate/case8.txt b/examples/huawei_2017/case_example/batch2/1Intermediate/case8.txt
new file mode 100644
index 00000000..ffdea9f2
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/1Intermediate/case8.txt
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diff --git a/examples/huawei_2017/case_example/batch2/1Intermediate/case9.txt b/examples/huawei_2017/case_example/batch2/1Intermediate/case9.txt
new file mode 100644
index 00000000..25732a53
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/1Intermediate/case9.txt
@@ -0,0 +1,2394 @@
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diff --git a/examples/huawei_2017/case_example/batch2/2Advanced/case0.txt b/examples/huawei_2017/case_example/batch2/2Advanced/case0.txt
new file mode 100644
index 00000000..e64ee4b5
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/2Advanced/case0.txt
@@ -0,0 +1,6464 @@
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diff --git a/examples/huawei_2017/case_example/batch2/2Advanced/case1.txt b/examples/huawei_2017/case_example/batch2/2Advanced/case1.txt
new file mode 100644
index 00000000..9c1a9bde
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/2Advanced/case1.txt
@@ -0,0 +1,6458 @@
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diff --git a/examples/huawei_2017/case_example/batch2/2Advanced/case2.txt b/examples/huawei_2017/case_example/batch2/2Advanced/case2.txt
new file mode 100644
index 00000000..d2185789
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/2Advanced/case2.txt
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diff --git a/examples/huawei_2017/case_example/batch2/2Advanced/case3.txt b/examples/huawei_2017/case_example/batch2/2Advanced/case3.txt
new file mode 100644
index 00000000..7aedb622
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/2Advanced/case3.txt
@@ -0,0 +1,6459 @@
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diff --git a/examples/huawei_2017/case_example/batch2/2Advanced/case4.txt b/examples/huawei_2017/case_example/batch2/2Advanced/case4.txt
new file mode 100644
index 00000000..61b443ea
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/2Advanced/case4.txt
@@ -0,0 +1,6440 @@
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diff --git a/examples/huawei_2017/case_example/batch2/2Advanced/case5.txt b/examples/huawei_2017/case_example/batch2/2Advanced/case5.txt
new file mode 100644
index 00000000..6920c109
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/2Advanced/case5.txt
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diff --git a/examples/huawei_2017/case_example/batch2/2Advanced/case6.txt b/examples/huawei_2017/case_example/batch2/2Advanced/case6.txt
new file mode 100644
index 00000000..5e1a6919
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/2Advanced/case6.txt
@@ -0,0 +1,6444 @@
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diff --git a/examples/huawei_2017/case_example/batch2/2Advanced/case7.txt b/examples/huawei_2017/case_example/batch2/2Advanced/case7.txt
new file mode 100644
index 00000000..67a5ffc8
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/2Advanced/case7.txt
@@ -0,0 +1,6454 @@
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diff --git a/examples/huawei_2017/case_example/batch2/2Advanced/case8.txt b/examples/huawei_2017/case_example/batch2/2Advanced/case8.txt
new file mode 100644
index 00000000..dfc7d676
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/2Advanced/case8.txt
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diff --git a/examples/huawei_2017/case_example/batch2/2Advanced/case9.txt b/examples/huawei_2017/case_example/batch2/2Advanced/case9.txt
new file mode 100644
index 00000000..8aa70d69
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch2/2Advanced/case9.txt
@@ -0,0 +1,6478 @@
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diff --git a/examples/huawei_2017/case_example/batch3/1Intermediate/case0.txt b/examples/huawei_2017/case_example/batch3/1Intermediate/case0.txt
new file mode 100644
index 00000000..7be7ef1d
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/1Intermediate/case0.txt
@@ -0,0 +1,2647 @@
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diff --git a/examples/huawei_2017/case_example/batch3/1Intermediate/case1.txt b/examples/huawei_2017/case_example/batch3/1Intermediate/case1.txt
new file mode 100644
index 00000000..f6b3ad5b
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/1Intermediate/case1.txt
@@ -0,0 +1,2653 @@
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diff --git a/examples/huawei_2017/case_example/batch3/1Intermediate/case2.txt b/examples/huawei_2017/case_example/batch3/1Intermediate/case2.txt
new file mode 100644
index 00000000..741dea14
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/1Intermediate/case2.txt
@@ -0,0 +1,2690 @@
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diff --git a/examples/huawei_2017/case_example/batch3/1Intermediate/case3.txt b/examples/huawei_2017/case_example/batch3/1Intermediate/case3.txt
new file mode 100644
index 00000000..0c585465
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/1Intermediate/case3.txt
@@ -0,0 +1,2683 @@
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diff --git a/examples/huawei_2017/case_example/batch3/1Intermediate/case4.txt b/examples/huawei_2017/case_example/batch3/1Intermediate/case4.txt
new file mode 100644
index 00000000..9d4958f4
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/1Intermediate/case4.txt
@@ -0,0 +1,2675 @@
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diff --git a/examples/huawei_2017/case_example/batch3/1Intermediate/case5.txt b/examples/huawei_2017/case_example/batch3/1Intermediate/case5.txt
new file mode 100644
index 00000000..52f0a706
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/1Intermediate/case5.txt
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diff --git a/examples/huawei_2017/case_example/batch3/1Intermediate/case6.txt b/examples/huawei_2017/case_example/batch3/1Intermediate/case6.txt
new file mode 100644
index 00000000..f033cd5f
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/1Intermediate/case6.txt
@@ -0,0 +1,2674 @@
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diff --git a/examples/huawei_2017/case_example/batch3/1Intermediate/case7.txt b/examples/huawei_2017/case_example/batch3/1Intermediate/case7.txt
new file mode 100644
index 00000000..8c10da51
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/1Intermediate/case7.txt
@@ -0,0 +1,2643 @@
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diff --git a/examples/huawei_2017/case_example/batch3/1Intermediate/case8.txt b/examples/huawei_2017/case_example/batch3/1Intermediate/case8.txt
new file mode 100644
index 00000000..157fdb79
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/1Intermediate/case8.txt
@@ -0,0 +1,2679 @@
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diff --git a/examples/huawei_2017/case_example/batch3/1Intermediate/case9.txt b/examples/huawei_2017/case_example/batch3/1Intermediate/case9.txt
new file mode 100644
index 00000000..1d213670
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/1Intermediate/case9.txt
@@ -0,0 +1,2671 @@
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diff --git a/examples/huawei_2017/case_example/batch3/2Advanced/case0.txt b/examples/huawei_2017/case_example/batch3/2Advanced/case0.txt
new file mode 100644
index 00000000..4e85e3a8
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/2Advanced/case0.txt
@@ -0,0 +1,6962 @@
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diff --git a/examples/huawei_2017/case_example/batch3/2Advanced/case1.txt b/examples/huawei_2017/case_example/batch3/2Advanced/case1.txt
new file mode 100644
index 00000000..d38ac22f
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/2Advanced/case1.txt
@@ -0,0 +1,6975 @@
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diff --git a/examples/huawei_2017/case_example/batch3/2Advanced/case2.txt b/examples/huawei_2017/case_example/batch3/2Advanced/case2.txt
new file mode 100644
index 00000000..c9c81004
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/2Advanced/case2.txt
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diff --git a/examples/huawei_2017/case_example/batch3/2Advanced/case3.txt b/examples/huawei_2017/case_example/batch3/2Advanced/case3.txt
new file mode 100644
index 00000000..ca052f52
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/2Advanced/case3.txt
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diff --git a/examples/huawei_2017/case_example/batch3/2Advanced/case4.txt b/examples/huawei_2017/case_example/batch3/2Advanced/case4.txt
new file mode 100644
index 00000000..72035a82
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/2Advanced/case4.txt
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diff --git a/examples/huawei_2017/case_example/batch3/2Advanced/case5.txt b/examples/huawei_2017/case_example/batch3/2Advanced/case5.txt
new file mode 100644
index 00000000..6d6fbeda
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/2Advanced/case5.txt
@@ -0,0 +1,6935 @@
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diff --git a/examples/huawei_2017/case_example/batch3/2Advanced/case6.txt b/examples/huawei_2017/case_example/batch3/2Advanced/case6.txt
new file mode 100644
index 00000000..3924a12b
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/2Advanced/case6.txt
@@ -0,0 +1,6974 @@
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diff --git a/examples/huawei_2017/case_example/batch3/2Advanced/case7.txt b/examples/huawei_2017/case_example/batch3/2Advanced/case7.txt
new file mode 100644
index 00000000..19b2ee35
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/2Advanced/case7.txt
@@ -0,0 +1,6982 @@
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diff --git a/examples/huawei_2017/case_example/batch3/2Advanced/case8.txt b/examples/huawei_2017/case_example/batch3/2Advanced/case8.txt
new file mode 100644
index 00000000..9995ec15
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/2Advanced/case8.txt
@@ -0,0 +1,7001 @@
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diff --git a/examples/huawei_2017/case_example/batch3/2Advanced/case9.txt b/examples/huawei_2017/case_example/batch3/2Advanced/case9.txt
new file mode 100644
index 00000000..96026e66
--- /dev/null
+++ b/examples/huawei_2017/case_example/batch3/2Advanced/case9.txt
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diff --git a/examples/huawei_2017/config.qwen.yaml b/examples/huawei_2017/config.qwen.yaml
new file mode 100644
index 00000000..2c690fa7
--- /dev/null
+++ b/examples/huawei_2017/config.qwen.yaml
@@ -0,0 +1,323 @@
+# 华为CodeCraft 2017 CDN优化 - 优化后的OpenEvolve配置
+# 基于OpenEvolve最佳实践
+
+max_iterations: 300
+checkpoint_interval: 25  # 降低到25以减少I/O开销
+random_seed: 42  # 确保可重现性
+
+# LLM配置 - 优化模型选择和权重
+llm:
+  models:
+    # 主力模型：Qwen-235B用于复杂推理（提高权重）
+    - name: "Qwen/Qwen3-235B-A22B-Instruct-2507"
+      weight: 0.6  # 增加权重，利用大模型的推理能力
+      api_key: "key"
+      api_base: "https://api-inference.modelscope.cn/v1"
+      temperature: 0.75  # 略微降低以提高代码质量
+      top_p: 0.90
+      max_tokens: 8192
+      timeout: 180
+
+    # 辅助模型：Qwen-30B用于快速迭代
+    - name: "Qwen/Qwen3-30B-A3B-Instruct-2507"
+      weight: 0.4
+      api_key: "key"
+      api_base: "https://api-inference.modelscope.cn/v1"
+      temperature: 0.85  # 稍高温度鼓励探索
+      top_p: 0.92
+      max_tokens: 6144
+      timeout: 120
+
+  # 评估器模型（用于LLM反馈）
+  evaluator_models:
+    - name: "Qwen/Qwen3-235B-A22B-Instruct-2507"
+      weight: 1.0
+      api_key: "key"
+      api_base: "https://api-inference.modelscope.cn/v1"
+      temperature: 0.6  # 降低温度以获得更客观的评估
+      max_tokens: 2048
+      timeout: 90
+
+# Prompt配置 - 这是最关键的部分
+prompt:
+  system_message: |
+    You are an ELITE algorithmic optimization specialist with deep expertise in:
+    • Combinatorial optimization & operations research
+    • Network flow algorithms & facility location problems  
+    • Graph theory & shortest path algorithms
+    • Greedy heuristics & local search methods
+    • Metaheuristics: simulated annealing, tabu search, genetic algorithms
+
+    ═══════════════════════════════════════════════════════════════════════
+    PROBLEM: HUAWEI CODECRAFT 2017 - CDN SERVER DEPLOYMENT OPTIMIZATION
+    ═══════════════════════════════════════════════════════════════════════
+
+    MATHEMATICAL FORMULATION:
+    This is a Multi-Commodity Capacitated Facility Location Problem (MC-CFLP):
+    
+    Minimize: Z = Σ(server_hardware[i,t] + node_deploy[i]) + Σ(flow[i,j,c] × link_cost[i,j])
+    
+    Subject to:
+    1. Demand satisfaction: Σ flow[i,c] >= demand[c]  ∀ consumers c
+    2. Link capacity: Σ flow[i,j,c] <= bandwidth[i,j]  ∀ directed links (i,j)
+    3. Server capacity: Σ outflow[i,c] <= server_capacity[i]  ∀ servers i
+    4. Flow conservation: inflow[j] = outflow[j]  ∀ intermediate nodes j
+    5. Binary deployment: server[i] ∈ {0,1}  ∀ nodes i
+
+    PROBLEM SCALE & CONSTRAINTS:
+    • Network: up to 10,000 nodes, 10,000 links per node
+    • Consumers: up to 10,000 consumer nodes with varying demands
+    • Servers: up to 10 capacity tiers (different hardware costs)
+    • Links: Directed with independent bandwidth in each direction
+    • Complexity: NP-Hard facility location + multi-commodity flow
+
+    ═══════════════════════════════════════════════════════════════════════
+    CRITICAL SUCCESS FACTORS (Ranked by Impact)
+    ═══════════════════════════════════════════════════════════════════════
+
+    1. FEASIBILITY (Pass/Fail - 40% weight via success_rate)
+       ❌ Invalid solution → combined_score = 0
+       ✅ Valid solution → Can compete for ranking
+       
+       MUST satisfy ALL constraints:
+       • Every consumer receives full demand
+       • No link bandwidth exceeded (check BOTH directions)
+       • No server capacity exceeded
+       • Valid paths through existing edges only
+
+    2. COST OPTIMIZATION (35% weight - PRIMARY ranking metric)
+       This is THE most important metric for competition ranking!
+       • Even 1% cost reduction matters significantly
+       • Focus: Minimize total_cost = deployment_cost + bandwidth_cost
+       • Trade-offs: More servers vs. longer paths
+       • Key insight: Balance local deployment vs. remote serving
+
+    3. EXECUTION SPEED (25% weight - CRITICAL, not negligible!)
+       Time Score Formula: exp(-avg_time / 10.0)
+       • 1s  → 0.905 (excellent)
+       • 5s  → 0.606 (good)
+       • 10s → 0.368 (acceptable)
+       • 20s → 0.135 (poor)
+       • 30s → timeout risk
+       
+       Target: < 5 seconds per test case for competitive time_score
+       Hard limit: 30 seconds timeout → solution rejected
+
+    SCORING FORMULA:
+    combined_score = 0.40×success_rate + 0.35×cost_score + 0.25×time_score
+                     └─────────────┘   └──────────────┘   └──────────────┘
+                      Feasibility         Cost (primary)     Speed (critical!)
+
+    ═══════════════════════════════════════════════════════════════════════
+    ALGORITHMIC STRATEGIES - HIERARCHICAL APPROACH
+    ═══════════════════════════════════════════════════════════════════════
+
+    PHASE 1: INTELLIGENT SERVER PLACEMENT (Most impactful!)
+    ───────────────────────────────────────────────────────────────────────
+    Strategy A - Demand-Driven Placement:
+      • Cluster consumers by network distance (use graph algorithms)
+      • Identify high-demand density regions → place servers at cluster centers
+      • Use network centrality metrics: betweenness, closeness, degree
+      • Consider: k-medoids clustering on consumer locations
+    
+    Strategy B - Cost-Benefit Analysis:
+      • For each potential server location, calculate:
+        deployment_cost[loc] vs. bandwidth_savings[loc]
+      • Prioritize locations where bandwidth savings > deployment cost
+      • Use incremental approach: add servers where ROI is highest
+    
+    Strategy C - Hub-and-Spoke Architecture:
+      • Identify natural network hubs (high connectivity nodes)
+      • Place larger servers at hubs to serve multiple consumers
+      • Use smaller servers for isolated high-demand areas
+      • Balance: hub concentration vs. geographic distribution
+
+    PHASE 2: OPTIMAL SERVER TYPE SELECTION
+    ───────────────────────────────────────────────────────────────────────
+    Key Insight: Server cost efficiency varies by type
+    
+    • Calculate: efficiency[type] = hardware_cost / capacity
+    • Right-sizing: Match capacity to aggregate local demand
+    • Economies of scale: Larger servers may have better cost/capacity
+    • Upgrade analysis: Compare cost of upgrading vs. adding new server
+    • Capacity buffer: Leave 10-20% headroom for flexibility
+
+    PHASE 3: FLOW ROUTING OPTIMIZATION
+    ───────────────────────────────────────────────────────────────────────
+    Primary: Min-Cost Flow Algorithm
+      • Use Dijkstra for shortest paths (weighted by link cost)
+      • Consider: successive shortest path algorithm
+      • Track remaining capacity on each link (directional!)
+    
+    Advanced: Load Balancing & Flow Splitting
+      • Distribute flow across multiple paths to avoid bottlenecks
+      • Use k-shortest paths algorithm for diversity
+      • Flow splitting: Serve consumers from multiple servers
+      • Capacity-aware routing: Avoid saturated links early
+    
+    Optimization: Path Reuse
+      • Identify common path segments across multiple flows
+      • Consolidate flows on shared segments to reduce total cost
+      • Use network flow decomposition techniques
+
+    PHASE 4: LOCAL SEARCH & REFINEMENT (Escape local optima!)
+    ───────────────────────────────────────────────────────────────────────
+    Moves to Consider:
+    1. Server Relocation: Move server to adjacent node if cost improves
+    2. Server Type Swap: Change capacity tier at existing location
+    3. Server Consolidation: Remove underutilized servers, reroute demands
+    4. Server Addition: Add servers where bandwidth costs are excessive
+    5. Demand Reassignment: Switch consumers to different servers
+    6. Path Refinement: Find alternative routes with lower total cost
+
+    Metaheuristics (Advanced):
+    • Simulated Annealing: Accept occasional cost increases (probability = exp(-ΔC/T))
+    • Tabu Search: Maintain memory of recent moves (avoid cycling)
+    • Iterative Local Search: Perturb solution, then local search
+    • Variable Neighborhood Descent: Explore different move types
+
+    ═══════════════════════════════════════════════════════════════════════
+    PRACTICAL IMPLEMENTATION GUIDELINES
+    ═══════════════════════════════════════════════════════════════════════
+
+    CODE OPTIMIZATION:
+    • Use efficient data structures: dict for O(1) lookup, heapq for priority queues
+    • Memoize shortest paths: Cache results to avoid recomputation
+    • Early termination: Stop search when improvements < threshold
+    • Incremental updates: Modify solution rather than rebuild from scratch
+    • Limit iterations: Balance quality vs. time (use time budgets)
+
+    DEBUGGING CHECKLIST:
+    ✓ Flow conservation: inflow = outflow at all intermediate nodes
+    ✓ Demand satisfaction: Each consumer receives >= required demand
+    ✓ Link capacity: Check BOTH directions independently
+    ✓ Server capacity: Total outflow <= server output capacity
+    ✓ Path validity: All edges exist in network graph
+    ✓ No negative flows or costs
+
+    COMMON PITFALLS TO AVOID:
+    ✗ Forgetting directional link capacities (A→B ≠ B→A)
+    ✗ Placing servers at consumer nodes (may not be cost-effective)
+    ✗ Ignoring server capacity constraints
+    ✗ Using only greedy placement (falls into local optima)
+    ✗ Over-optimizing for cost while ignoring time constraints
+    ✗ Not validating solution feasibility before returning
+
+    ═══════════════════════════════════════════════════════════════════════
+    EVOLUTION STRATEGY
+    ═══════════════════════════════════════════════════════════════════════
+
+    BUILD INCREMENTALLY:
+    • Start with current working solution (don't rewrite from scratch!)
+    • Make targeted improvements to specific components
+    • Test each change for correctness before adding complexity
+    • Preserve what works, enhance what doesn't
+
+    FOCUS AREAS FOR EVOLUTION:
+    Priority 1: Server placement logic (highest impact on cost)
+    Priority 2: Flow routing algorithm (significant cost impact)
+    Priority 3: Local search refinement (escape local optima)
+    Priority 4: Time optimization (critical for score!)
+
+    BALANCE EXPLORATION vs EXPLOITATION:
+    • Early iterations: Try diverse algorithmic approaches
+    • Middle iterations: Refine promising strategies
+    • Late iterations: Fine-tune parameters, optimize performance
+
+    ═══════════════════════════════════════════════════════════════════════
+    CODE STRUCTURE REQUIREMENTS
+    ═══════════════════════════════════════════════════════════════════════
+
+    ONLY MODIFY CODE BETWEEN MARKERS:
+    # EVOLVE-BLOCK-START
+    def solve_cdn_deployment(...):
+        # Your algorithm here
+        pass
+    # EVOLVE-BLOCK-END
+
+    DO NOT CHANGE:
+    • NetworkGraph class (already implemented)
+    • Input parsing (parse_input function)
+    • Output formatting (format_output function)
+    • Main entry point (run_solution function)
+
+    RETURN FORMAT:
+    return (total_cost, paths)
+    where paths = [(path_nodes, consumer_id, bandwidth, server_type), ...]
+
+    ═══════════════════════════════════════════════════════════════════════
+    IMPORTANT: Keep ALL code and comments in ENGLISH to avoid encoding issues.
+    ═══════════════════════════════════════════════════════════════════════
+
+  # 提示工程配置
+  num_top_programs: 4      # 增加到4个，获得更多优秀示例
+  num_diverse_programs: 2  # 保持2个多样性示例，平衡探索
+  include_artifacts: true  # 启用artifacts获得错误反馈
+
+  # 模板多样性（避免陷入局部最优）
+  use_template_stochasticity: true
+  template_variations:
+    opening:
+      - "Let's optimize this CDN deployment algorithm:"
+      - "Time to improve the server placement strategy:"
+      - "Analyzing the current solution for enhancements:"
+      - "Evolving the network optimization approach:"
+
+    strategy_intro:
+      - "Consider the following algorithmic improvements:"
+      - "Key optimization opportunities:"
+      - "Strategic enhancements to explore:"
+      - "Promising directions for evolution:"
+
+    cost_focus:
+      - "Focus on reducing total deployment and bandwidth costs"
+      - "Prioritize cost-effective server placement decisions"
+      - "Balance server deployment vs. bandwidth rental expenses"
+      - "Minimize the combined cost of infrastructure and traffic"
+
+# MAP-Elites数据库配置 - 优化种群管理
+database:
+  population_size: 100      # 增加到100以保持更多样性
+  archive_size: 30          # 增加archive提高精英保留
+  num_islands: 5            # 增加到5个岛屿，提高多样性
+  migration_interval: 25    # 增加迁移间隔，让岛屿充分进化
+  elite_selection_ratio: 0.30  # 增加精英选择比例
+  exploitation_ratio: 0.70     # 增加开发比例（因为问题已明确定义）
+
+  # 特征维度（质量-多样性算法的核心）
+  feature_dimensions:
+    - "complexity"          # 内置：代码复杂度
+    - "avg_cost"           # 自定义：平均成本（来自评估器）
+    # 注意：avg_time已移除，因为stage1评估不返回此指标
+    # 时间因素已通过combined_score中的time_score权重体现
+
+  # 每个维度的分箱数（可选）
+  feature_bins:
+    complexity: 12         # 12个复杂度档次
+    avg_cost: 15           # 15个成本档次
+
+  # 相似度控制
+  similarity_threshold: 0.92  # 降低到0.92允许更多相似但不同的解
+  embedding_model: "text-embedding-3-small"
+
+# 评估器配置 - 优化评估流程
+evaluator:
+  timeout: 90                    # 单个测试用例超时
+  parallel_evaluations: 4        # 增加并行数以加速（如果资源允许）
+  cascade_evaluation: true       # 启用级联评估
+  enable_artifacts: true         # 启用artifacts反馈
+  use_llm_feedback: false        # 关闭LLM反馈以节省时间（可选）
+
+  # Artifacts配置
+#  max_artifact_bytes: 8192       # 增加到8KB以捕获更多错误信息
+#  artifact_security_filter: true
+
+# 演化设置
+diff_based_evolution: true       # 使用基于差异的演化（渐进优化）
+max_code_length: 30000           # 增加到30K以允许更复杂的解决方案
+
+# 日志和监控
+logging:
+  level: "INFO"
+  log_evaluations: true
+  log_llm_calls: false  # 关闭以减少日志量
\ No newline at end of file
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diff --git a/examples/huawei_2017/evaluator.py b/examples/huawei_2017/evaluator.py
new file mode 100644
index 00000000..7c9b9d53
--- /dev/null
+++ b/examples/huawei_2017/evaluator.py
@@ -0,0 +1,443 @@
+"""
+Evaluator for Huawei CodeCraft 2017 CDN optimization problem
+"""
+import importlib.util
+import time
+import concurrent.futures
+import traceback
+import os
+import glob
+from pathlib import Path
+from typing import Dict, List, Tuple
+from openevolve.evaluation_result import EvaluationResult
+
+# Use a large number instead of infinity for JSON compatibility
+INFINITY_COST = 999999999
+
+
+def run_with_timeout(func, args=(), kwargs={}, timeout_seconds=30):
+    """Run a function with timeout"""
+    with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
+        future = executor.submit(func, *args, **kwargs)
+        try:
+            result = future.result(timeout=timeout_seconds)
+            return result
+        except concurrent.futures.TimeoutError:
+            raise TimeoutError(f"Function timed out after {timeout_seconds} seconds")
+
+
+def validate_solution(data: dict, paths: List[Tuple[List[int], int, int, int]]) -> Tuple[bool, str, int]:
+    """
+    Validate solution correctness
+
+    Returns:
+        (is_valid, error_message, actual_cost)
+    """
+    num_nodes = data['num_nodes']
+    edges = data['edges']
+    consumers = data['consumers']
+    server_types = {tid: (cap, cost) for tid, cap, cost in data['server_types']}
+    node_costs = {nid: cost for nid, cost in data['node_deploy_costs']}
+
+    # Build edge capacity map (bidirectional)
+    edge_capacity = {}
+    edge_costs = {}
+    for u, v, bw, cost in edges:
+        edge_capacity[(u, v)] = bw
+        edge_capacity[(v, u)] = bw
+        edge_costs[(u, v)] = cost
+        edge_costs[(v, u)] = cost
+
+    # Track deployed servers and used bandwidth
+    deployed_servers = {}  # node -> (server_type, used_capacity)
+    link_usage = {}  # (u, v) -> used_bandwidth
+    consumer_satisfied = {cid: 0 for cid, _, _ in consumers}
+
+    total_server_cost = 0
+    total_bandwidth_cost = 0
+
+    # Build consumer map
+    consumer_map = {cid: (node, demand) for cid, node, demand in consumers}
+
+    for path, consumer_id, bandwidth, server_type in paths:
+        if len(path) == 0:
+            return False, f"Empty path for consumer {consumer_id}", 0
+
+        if consumer_id not in consumer_map:
+            return False, f"Invalid consumer ID {consumer_id}", 0
+
+        consumer_node, _ = consumer_map[consumer_id]
+        server_node = path[0]
+
+        # The path should end at the consumer's connected network node
+        # Check if path correctly reaches consumer
+        if len(path) == 1:
+            # Direct connection: server at same node as consumer
+            if path[0] != consumer_node:
+                return False, f"Single-node path {path[0]} doesn't match consumer {consumer_id} node {consumer_node}", 0
+        else:
+            # Multi-hop path: last node should be consumer's connected node
+            if path[-1] != consumer_node:
+                return False, f"Path end {path[-1]} doesn't match consumer {consumer_id} node {consumer_node}", 0
+
+        # Check server deployment
+        if server_node not in deployed_servers:
+            if server_type not in server_types:
+                return False, f"Invalid server type {server_type}", 0
+
+            capacity, hw_cost = server_types[server_type]
+            deploy_cost = node_costs.get(server_node, 0)
+            total_server_cost += hw_cost + deploy_cost
+            deployed_servers[server_node] = [server_type, 0, capacity]
+
+        # Check server capacity
+        _, used_cap, max_cap = deployed_servers[server_node]
+        if used_cap + bandwidth > max_cap:
+            return False, f"Server at node {server_node} capacity exceeded", 0
+
+        deployed_servers[server_node][1] += bandwidth
+
+        # Check path validity and bandwidth (only for multi-hop paths)
+        for i in range(len(path) - 1):
+            u, v = path[i], path[i + 1]
+
+            # Check edge exists
+            if (u, v) not in edge_capacity:
+                return False, f"Edge ({u}, {v}) does not exist", 0
+
+            # Track bandwidth usage
+            if (u, v) not in link_usage:
+                link_usage[(u, v)] = 0
+            link_usage[(u, v)] += bandwidth
+
+            # Check capacity
+            if link_usage[(u, v)] > edge_capacity[(u, v)]:
+                return False, f"Edge ({u}, {v}) capacity exceeded: {link_usage[(u, v)]} > {edge_capacity[(u, v)]}", 0
+
+            # Add bandwidth cost
+            total_bandwidth_cost += bandwidth * edge_costs[(u, v)]
+
+        # Track consumer satisfaction
+        consumer_satisfied[consumer_id] += bandwidth
+
+    # Check all consumers are satisfied
+    for consumer_id, connected_node, demand in consumers:
+        if consumer_satisfied[consumer_id] < demand:
+            return False, f"Consumer {consumer_id} not satisfied: {consumer_satisfied[consumer_id]} < {demand}", 0
+
+    total_cost = total_server_cost + total_bandwidth_cost
+    return True, "", total_cost
+
+
+def load_test_cases(case_dir: str) -> List[Tuple[str, str]]:
+    """Load all test cases from directory"""
+    case_files = []
+
+    # Search in batch directories
+    for batch_dir in glob.glob(os.path.join(case_dir, "batch*")):
+        for level_dir in glob.glob(os.path.join(batch_dir, "*")):
+            for case_file in glob.glob(os.path.join(level_dir, "case*.txt")):
+                case_files.append(case_file)
+
+    return case_files
+
+
+def evaluate(program_path: str):
+    """
+    Evaluate the program on multiple test cases
+
+    Args:
+        program_path: Path to the program file
+
+    Returns:
+        EvaluationResult with metrics
+    """
+    # Get case directory (relative to program or this evaluator file)
+    program_dir = os.path.dirname(os.path.abspath(program_path))
+    case_dir = os.path.join(program_dir, "case_example")
+
+    # If case_example doesn't exist relative to program, try relative to this evaluator
+    if not os.path.exists(case_dir):
+        evaluator_dir = os.path.dirname(os.path.abspath(__file__))
+        case_dir = os.path.join(evaluator_dir, "case_example")
+
+    if not os.path.exists(case_dir):
+        return EvaluationResult(
+            metrics={
+                "valid_solutions": 0.0,
+                "avg_cost": INFINITY_COST,
+                "combined_score": 0.0,
+                "error": "Test case directory not found"
+            },
+            artifacts={
+                "error_type": "MissingTestCases",
+                "error_message": f"Case directory not found: {case_dir}",
+                "suggestion": "Ensure case_example directory exists with test cases"
+            }
+        )
+
+    try:
+        # Load the program
+        spec = importlib.util.spec_from_file_location("program", program_path)
+        program = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(program)
+
+        # Check required function
+        if not hasattr(program, "run_solution"):
+            return EvaluationResult(
+                metrics={
+                    "valid_solutions": 0.0,
+                    "avg_cost": INFINITY_COST,
+                    "combined_score": 0.0,
+                    "error": "Missing run_solution function"
+                },
+                artifacts={
+                    "error_type": "MissingFunction",
+                    "error_message": "Program missing run_solution function",
+                    "suggestion": "Ensure your program has a run_solution(input_text) function"
+                }
+            )
+
+        # Load test cases (limit to 5 for faster evaluation)
+        test_cases = load_test_cases(case_dir)[:5]
+
+        if not test_cases:
+            return EvaluationResult(
+                metrics={
+                    "valid_solutions": 0.0,
+                    "avg_cost": INFINITY_COST,
+                    "combined_score": 0.0,
+                    "error": "No test cases found"
+                },
+                artifacts={
+                    "error_type": "NoTestCases",
+                    "error_message": f"No test case files found in {case_dir}",
+                    "suggestion": "Verify test case files exist in case_example/batch*/*/case*.txt"
+                }
+            )
+
+        # Run on test cases
+        results = []
+        valid_count = 0
+        total_time = 0
+        error_details = []
+
+        for case_file in test_cases:
+            try:
+                with open(case_file, 'r', encoding='utf-8') as f:
+                    input_text = f.read()
+
+                # Parse input for validation
+                data = program.parse_input(input_text)
+
+                start_time = time.time()
+                cost, output = run_with_timeout(
+                    program.run_solution,
+                    args=(input_text,),
+                    timeout_seconds=30
+                )
+                elapsed = time.time() - start_time
+                total_time += elapsed
+
+                # Parse output to get paths
+                output_lines = [line.strip() for line in output.strip().split('\n') if line.strip()]
+                if len(output_lines) < 2:
+                    error_details.append(f"{Path(case_file).name}: Invalid output format")
+                    continue
+
+                num_paths = int(output_lines[0])
+                paths = []
+
+                # Start from line 1 (after the count line), since we filtered out empty lines
+                for i in range(1, min(len(output_lines), num_paths + 1)):
+                    parts = output_lines[i].split()
+                    if len(parts) < 4:  # At least: server_node consumer_id bandwidth server_type
+                        continue
+
+                    # Parse path: node1 node2 ... nodeN consumer_id bandwidth server_type
+                    # Last 3 elements are: consumer_id, bandwidth, server_type
+                    path_nodes = [int(x) for x in parts[:-3]]
+                    consumer_id = int(parts[-3])
+                    bandwidth = int(parts[-2])
+                    server_type = int(parts[-1])
+
+                    paths.append((path_nodes, consumer_id, bandwidth, server_type))
+
+                # Validate solution
+                is_valid, error_msg, actual_cost = validate_solution(data, paths)
+
+                if is_valid:
+                    valid_count += 1
+                    results.append({
+                        'case': Path(case_file).name,
+                        'cost': actual_cost,
+                        'time': elapsed,
+                        'valid': True
+                    })
+                else:
+                    error_details.append(f"{Path(case_file).name}: {error_msg}")
+                    results.append({
+                        'case': Path(case_file).name,
+                        'cost': INFINITY_COST,
+                        'time': elapsed,
+                        'valid': False,
+                        'error': error_msg
+                    })
+
+            except TimeoutError as e:
+                error_details.append(f"{Path(case_file).name}: Timeout")
+                results.append({'case': Path(case_file).name, 'cost': INFINITY_COST, 'valid': False, 'error': 'Timeout'})
+            except Exception as e:
+                error_details.append(f"{Path(case_file).name}: {str(e)}")
+                results.append({'case': Path(case_file).name, 'cost': INFINITY_COST, 'valid': False, 'error': str(e)})
+
+        # Calculate metrics
+        if valid_count == 0:
+            return EvaluationResult(
+                metrics={
+                    "valid_solutions": 0.0,
+                    "avg_cost": INFINITY_COST,
+                    "combined_score": 0.0,
+                    "error": "All test cases failed"
+                },
+                artifacts={
+                    "error_type": "AllCasesFailed",
+                    "error_message": f"All {len(test_cases)} test cases failed",
+                    "error_details": error_details[:5],
+                    "suggestion": "Check algorithm correctness and constraint handling"
+                }
+            )
+
+        valid_costs = [r['cost'] for r in results if r['valid']]
+        avg_cost = sum(valid_costs) / len(valid_costs)
+        success_rate = valid_count / len(test_cases)
+        avg_time = total_time / len(test_cases)
+
+        # Normalize cost to score (lower cost = higher score)
+        # Use reference cost of 10000 as baseline
+        cost_score = 10000.0 / (avg_cost + 1.0)
+
+        # Time score (faster is better)
+        # Exponential decay: 1.0 at 0s, ~0.5 at 5s, ~0.1 at 15s, approaches 0 for longer times
+        # This heavily penalizes slow solutions since contest limits are 30s-90s
+        import math
+        time_score = math.exp(-avg_time / 10.0)  # Decay factor of 10 seconds
+
+        # For very fast solutions (< 1s), give bonus
+        if avg_time < 1.0:
+            time_score = min(1.0, time_score * 1.2)
+
+        # Combined score: 40% success rate + 35% cost + 25% time
+        # Increased time weight from 10% to 25% since it's a critical ranking factor
+        combined_score = 0.4 * success_rate + 0.35 * min(1.0, cost_score) + 0.25 * time_score
+
+        return EvaluationResult(
+            metrics={
+                "valid_solutions": success_rate,
+                "avg_cost": avg_cost,
+                "avg_time": avg_time,
+                "cost_score": cost_score,
+                "time_score": time_score,
+                "combined_score": combined_score,
+            },
+            artifacts={
+                "num_test_cases": len(test_cases),
+                "num_valid": valid_count,
+                "results": results[:3],  # First 3 results
+                "avg_time_per_case": f"{avg_time:.2f}s",
+                "total_time": f"{total_time:.2f}s",
+                "time_breakdown": {
+                    "fastest": f"{min(r['time'] for r in results if r['valid']):.2f}s" if valid_count > 0 else "N/A",
+                    "slowest": f"{max(r['time'] for r in results if r['valid']):.2f}s" if valid_count > 0 else "N/A",
+                },
+                "scoring_weights": {
+                    "success_rate": "40%",
+                    "cost": "35%",
+                    "time": "25%"
+                }
+            }
+        )
+
+    except Exception as e:
+        print(f"Evaluation failed: {str(e)}")
+        print(traceback.format_exc())
+
+        return EvaluationResult(
+            metrics={
+                "valid_solutions": 0.0,
+                "avg_cost": INFINITY_COST,
+                "combined_score": 0.0,
+                "error": str(e)
+            },
+            artifacts={
+                "error_type": type(e).__name__,
+                "error_message": str(e),
+                "full_traceback": traceback.format_exc(),
+                "suggestion": "Check for syntax errors or import issues"
+            }
+        )
+
+
+def evaluate_stage1(program_path: str):
+    """Stage 1: Quick validation with single test case"""
+    program_dir = os.path.dirname(os.path.abspath(program_path))
+    case_dir = os.path.join(program_dir, "case_example")
+
+    # If case_example doesn't exist relative to program, try relative to this evaluator
+    if not os.path.exists(case_dir):
+        evaluator_dir = os.path.dirname(os.path.abspath(__file__))
+        case_dir = os.path.join(evaluator_dir, "case_example")
+
+    try:
+        # Load program
+        spec = importlib.util.spec_from_file_location("program", program_path)
+        program = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(program)
+
+        if not hasattr(program, "run_solution"):
+            return EvaluationResult(
+                metrics={"runs_successfully": 0.0, "combined_score": 0.0},
+                artifacts={"error": "Missing run_solution function"}
+            )
+
+        # Load one test case
+        test_cases = load_test_cases(case_dir)[:1]
+        if not test_cases:
+            return EvaluationResult(
+                metrics={"runs_successfully": 0.0, "combined_score": 0.0},
+                artifacts={"error": "No test cases found"}
+            )
+
+        with open(test_cases[0], 'r', encoding='utf-8') as f:
+            input_text = f.read()
+
+        # Try to run
+        cost, output = run_with_timeout(program.run_solution, args=(input_text,), timeout_seconds=10)
+
+        # Basic validation
+        if cost > 0 and len(output) > 0:
+            return EvaluationResult(
+                metrics={"runs_successfully": 1.0, "combined_score": 0.5},
+                artifacts={"stage1_cost": cost}
+            )
+        else:
+            return EvaluationResult(
+                metrics={"runs_successfully": 0.5, "combined_score": 0.25},
+                artifacts={"warning": "Solution returned but may be invalid"}
+            )
+
+    except TimeoutError:
+        return EvaluationResult(
+            metrics={"runs_successfully": 0.0, "combined_score": 0.0},
+            artifacts={"error": "Timeout"}
+        )
+    except Exception as e:
+        return EvaluationResult(
+            metrics={"runs_successfully": 0.0, "combined_score": 0.0},
+            artifacts={"error": str(e)}
+        )
+
+
+def evaluate_stage2(program_path: str):
+    """Stage 2: Full evaluation"""
+    return evaluate(program_path)
diff --git a/examples/huawei_2017/initial_program.py b/examples/huawei_2017/initial_program.py
new file mode 100644
index 00000000..be57226b
--- /dev/null
+++ b/examples/huawei_2017/initial_program.py
@@ -0,0 +1,256 @@
+"""
+Huawei CodeCraft 2017 - CDN Server Deployment Optimization
+Initial baseline solution using greedy heuristics
+"""
+import sys
+from typing import List, Tuple, Dict, Set
+import heapq
+
+
+class NetworkGraph:
+    """Network graph representation"""
+    def __init__(self, num_nodes: int):
+        self.num_nodes = num_nodes
+        self.edges: Dict[int, List[Tuple[int, int, int]]] = {i: [] for i in range(num_nodes)}
+
+    def add_edge(self, u: int, v: int, bandwidth: int, cost: int):
+        """Add bidirectional edge with bandwidth and cost"""
+        self.edges[u].append((v, bandwidth, cost))
+        self.edges[v].append((u, bandwidth, cost))
+
+    def dijkstra(self, start: int, end: int) -> Tuple[List[int], int]:
+        """Find shortest path by cost"""
+        dist = [float('inf')] * self.num_nodes
+        parent = [-1] * self.num_nodes
+        dist[start] = 0
+
+        pq = [(0, start)]
+        visited = set()
+
+        while pq:
+            d, u = heapq.heappop(pq)
+            if u in visited:
+                continue
+            visited.add(u)
+
+            if u == end:
+                break
+
+            for v, bw, cost in self.edges[u]:
+                if dist[u] + cost < dist[v]:
+                    dist[v] = dist[u] + cost
+                    parent[v] = u
+                    heapq.heappush(pq, (dist[v], v))
+
+        # Reconstruct path
+        if parent[end] == -1 and start != end:
+            return [], float('inf')
+
+        path = []
+        curr = end
+        while curr != -1:
+            path.append(curr)
+            curr = parent[curr]
+        path.reverse()
+
+        return path, dist[end]
+
+
+# EVOLVE-BLOCK-START
+def solve_cdn_deployment(
+    num_nodes: int,
+    edges: List[Tuple[int, int, int, int]],  # (u, v, bandwidth, cost_per_unit)
+    consumers: List[Tuple[int, int, int]],  # (consumer_id, connected_node, demand)
+    server_types: List[Tuple[int, int, int]],  # (type_id, capacity, hardware_cost)
+    node_deploy_costs: List[Tuple[int, int]]  # (node_id, deploy_cost)
+) -> Tuple[int, List[Tuple[List[int], int, int, int]]]:
+    """
+    Solve CDN server deployment problem using simple greedy strategy
+
+    Strategy: Deploy one server per consumer at their connected node
+    This avoids bandwidth constraint issues by minimizing path lengths
+
+    Returns:
+        total_cost: Total deployment and bandwidth cost
+        paths: List of (path, consumer_id, bandwidth, server_type)
+    """
+    # Create deploy cost map
+    deploy_cost_map = {node_id: cost for node_id, cost in node_deploy_costs}
+
+    # Create server type map
+    server_map = {type_id: (capacity, hw_cost) for type_id, capacity, hw_cost in server_types}
+
+    total_cost = 0
+    paths = []
+    deployed_servers: Dict[int, Tuple[int, int, int]] = {}  # node -> (server_type, capacity, used)
+
+    # Sort consumers by demand (highest first) for better server type selection
+    sorted_consumers = sorted(consumers, key=lambda x: x[2], reverse=True)
+
+    for consumer_id, connected_node, demand in sorted_consumers:
+        server_node = connected_node
+
+        # Check if we already have a server at this node
+        if server_node not in deployed_servers:
+            # Choose appropriate server type
+            # Pick the smallest server type that can handle this consumer
+            chosen_type = None
+            for type_id, (capacity, hw_cost) in sorted(server_map.items(), key=lambda x: x[1][0]):
+                if capacity >= demand:
+                    chosen_type = type_id
+                    break
+
+            if chosen_type is None:
+                # Use largest server if none fit
+                chosen_type = max(server_map.keys(), key=lambda t: server_map[t][0])
+
+            capacity, hw_cost = server_map[chosen_type]
+            deploy_cost = deploy_cost_map.get(server_node, 0)
+
+            # Add deployment cost
+            total_cost += hw_cost + deploy_cost
+
+            # Record deployed server
+            deployed_servers[server_node] = (chosen_type, capacity, 0)
+
+        # Get server info
+        server_type, capacity, used = deployed_servers[server_node]
+
+        # Check if server can handle this consumer
+        if used + demand > capacity:
+            # Need to upgrade or add another server
+            # For simplicity, deploy a new larger server
+            # Find a server type that can handle total demand
+            total_demand = used + demand
+
+            for type_id, (cap, hw_cost) in sorted(server_map.items(), key=lambda x: x[1][0]):
+                if cap >= total_demand:
+                    # Only pay additional cost if upgrading
+                    old_hw_cost = server_map[server_type][1]
+                    if hw_cost > old_hw_cost:
+                        total_cost += (hw_cost - old_hw_cost)
+
+                    server_type = type_id
+                    capacity = cap
+                    break
+
+            deployed_servers[server_node] = (server_type, capacity, total_demand)
+        else:
+            # Server can handle it
+            deployed_servers[server_node] = (server_type, capacity, used + demand)
+
+        # Path is just the server node itself (server and consumer at same node)
+        path = [server_node]
+
+        # No bandwidth cost since server is co-located with consumer
+        # Record path
+        paths.append((path, consumer_id, demand, server_type))
+
+    return total_cost, paths
+# EVOLVE-BLOCK-END
+
+
+def parse_input(input_text: str) -> dict:
+    """Parse input file format"""
+    lines = [line.strip() for line in input_text.strip().split('\n') if line.strip()]
+    idx = 0
+
+    # First line: num_nodes, num_edges, num_consumers
+    parts = lines[idx].split()
+    num_nodes, num_edges, num_consumers = int(parts[0]), int(parts[1]), int(parts[2])
+    idx += 1
+
+    # Server types
+    server_types = []
+    while idx < len(lines):
+        parts = lines[idx].split()
+        if len(parts) == 3 and all(p.isdigit() for p in parts):
+            type_id, capacity, hw_cost = int(parts[0]), int(parts[1]), int(parts[2])
+            server_types.append((type_id, capacity, hw_cost))
+            idx += 1
+        else:
+            break
+
+    # Node deploy costs
+    node_deploy_costs = []
+    while idx < len(lines):
+        parts = lines[idx].split()
+        if len(parts) == 2 and all(p.isdigit() for p in parts):
+            node_id, deploy_cost = int(parts[0]), int(parts[1])
+            node_deploy_costs.append((node_id, deploy_cost))
+            idx += 1
+        else:
+            break
+
+    # Edges
+    edges = []
+    while idx < len(lines):
+        parts = lines[idx].split()
+        if len(parts) == 4 and all(p.isdigit() for p in parts):
+            u, v, bw, cost = int(parts[0]), int(parts[1]), int(parts[2]), int(parts[3])
+            edges.append((u, v, bw, cost))
+            idx += 1
+        else:
+            break
+
+    # Consumers
+    consumers = []
+    while idx < len(lines):
+        parts = lines[idx].split()
+        if len(parts) == 3 and all(p.isdigit() for p in parts):
+            consumer_id, connected_node, demand = int(parts[0]), int(parts[1]), int(parts[2])
+            consumers.append((consumer_id, connected_node, demand))
+            idx += 1
+        else:
+            break
+
+    return {
+        'num_nodes': num_nodes,
+        'num_edges': num_edges,
+        'num_consumers': num_consumers,
+        'server_types': server_types,
+        'node_deploy_costs': node_deploy_costs,
+        'edges': edges,
+        'consumers': consumers
+    }
+
+
+def format_output(paths: List[Tuple[List[int], int, int, int]]) -> str:
+    """Format output according to specification"""
+    output_lines = [str(len(paths)), ""]
+
+    for path, consumer_id, bandwidth, server_type in paths:
+        path_str = " ".join(map(str, path))
+        line = f"{path_str} {consumer_id} {bandwidth} {server_type}"
+        output_lines.append(line)
+
+    return "\n".join(output_lines)
+
+
+def run_solution(input_text: str) -> Tuple[int, str]:
+    """Main entry point for evolution"""
+    data = parse_input(input_text)
+
+    total_cost, paths = solve_cdn_deployment(
+        data['num_nodes'],
+        data['edges'],
+        data['consumers'],
+        data['server_types'],
+        data['node_deploy_costs']
+    )
+
+    output_text = format_output(paths)
+    return total_cost, output_text
+
+
+if __name__ == "__main__":
+    # Read from stdin or file
+    if len(sys.argv) > 1:
+        with open(sys.argv[1], 'r', encoding='utf-8') as f:
+            input_text = f.read()
+    else:
+        input_text = sys.stdin.read()
+
+    total_cost, output_text = run_solution(input_text)
+    print(f"Total cost: {total_cost}")
+    print(output_text)
diff --git a/examples/huawei_2017/run_evolution.bat b/examples/huawei_2017/run_evolution.bat
new file mode 100644
index 00000000..3efc8a65
--- /dev/null
+++ b/examples/huawei_2017/run_evolution.bat
@@ -0,0 +1,85 @@
+@echo off
+REM Script to run OpenEvolve on Huawei 2017 CDN optimization problem (Windows)
+
+setlocal enabledelayedexpansion
+
+echo Starting OpenEvolve evolution for Huawei CodeCraft 2017 CDN Optimization
+echo ========================================================================
+echo.
+
+REM Check if running from correct directory
+if not exist "initial_program.py" (
+    echo Error: Must run from examples\huawei_2017 directory
+    exit /b 1
+)
+
+REM Check if case examples exist
+if not exist "case_example" (
+    echo Error: case_example directory not found
+    exit /b 1
+)
+
+REM Default values
+set ITERATIONS=300
+set CONFIG=config.qwen.yaml
+set CHECKPOINT=
+
+REM Parse arguments
+:parse_args
+if "%~1"=="" goto run_evolution
+if "%~1"=="--iterations" (
+    set ITERATIONS=%~2
+    shift
+    shift
+    goto parse_args
+)
+if "%~1"=="--config" (
+    set CONFIG=%~2
+    shift
+    shift
+    goto parse_args
+)
+if "%~1"=="--checkpoint" (
+    set CHECKPOINT=%~2
+    shift
+    shift
+    goto parse_args
+)
+if "%~1"=="--help" (
+    echo Usage: %0 [OPTIONS]
+    echo.
+    echo Options:
+    echo   --iterations N      Number of evolution iterations (default: 300^)
+    echo   --config FILE       Config file to use (default: config.qwen.yaml^)
+    echo   --checkpoint DIR    Resume from checkpoint directory
+    echo   --help              Show this help message
+    exit /b 0
+)
+echo Unknown option: %~1
+echo Use --help for usage information
+exit /b 1
+
+:run_evolution
+REM Build command
+set CMD=python ..\..\openevolve-run.py initial_program.py evaluator.py --config %CONFIG% --iterations %ITERATIONS%
+
+if not "%CHECKPOINT%"=="" (
+    set CMD=%CMD% --checkpoint %CHECKPOINT%
+)
+
+echo Configuration:
+echo   Iterations: %ITERATIONS%
+echo   Config: %CONFIG%
+if not "%CHECKPOINT%"=="" (
+    echo   Checkpoint: %CHECKPOINT%
+)
+echo.
+echo Running: %CMD%
+echo.
+
+REM Run evolution
+%CMD%
+
+echo.
+echo Evolution complete! Check openevolve_output\ for results.
+echo Best solution: openevolve_output\best\best_program.py
diff --git a/examples/huawei_2017/run_evolution.sh b/examples/huawei_2017/run_evolution.sh
new file mode 100644
index 00000000..da495be2
--- /dev/null
+++ b/examples/huawei_2017/run_evolution.sh
@@ -0,0 +1,82 @@
+#!/bin/bash
+# Script to run OpenEvolve on Huawei 2017 CDN optimization problem
+
+set -e
+
+echo "Starting OpenEvolve evolution for Huawei CodeCraft 2017 CDN Optimization"
+echo "========================================================================"
+echo ""
+
+# Check if running from correct directory
+if [ ! -f "initial_program.py" ]; then
+    echo "Error: Must run from examples/huawei_2017 directory"
+    exit 1
+fi
+
+# Check if case examples exist
+if [ ! -d "case_example" ]; then
+    echo "Error: case_example directory not found"
+    exit 1
+fi
+
+# Default values
+ITERATIONS=300
+CONFIG="config.qwen.yaml"
+CHECKPOINT=""
+
+# Parse arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --iterations)
+            ITERATIONS="$2"
+            shift 2
+            ;;
+        --config)
+            CONFIG="$2"
+            shift 2
+            ;;
+        --checkpoint)
+            CHECKPOINT="$2"
+            shift 2
+            ;;
+        --help)
+            echo "Usage: $0 [OPTIONS]"
+            echo ""
+            echo "Options:"
+            echo "  --iterations N      Number of evolution iterations (default: 300)"
+            echo "  --config FILE       Config file to use (default: config.qwen.yaml)"
+            echo "  --checkpoint DIR    Resume from checkpoint directory"
+            echo "  --help              Show this help message"
+            exit 0
+            ;;
+        *)
+            echo "Unknown option: $1"
+            echo "Use --help for usage information"
+            exit 1
+            ;;
+    esac
+done
+
+# Build command
+CMD="python ../../openevolve-run.py initial_program.py evaluator.py --config $CONFIG --iterations $ITERATIONS"
+
+if [ -n "$CHECKPOINT" ]; then
+    CMD="$CMD --checkpoint $CHECKPOINT"
+fi
+
+echo "Configuration:"
+echo "  Iterations: $ITERATIONS"
+echo "  Config: $CONFIG"
+if [ -n "$CHECKPOINT" ]; then
+    echo "  Checkpoint: $CHECKPOINT"
+fi
+echo ""
+echo "Running: $CMD"
+echo ""
+
+# Run evolution
+$CMD
+
+echo ""
+echo "Evolution complete! Check openevolve_output/ for results."
+echo "Best solution: openevolve_output/best/best_program.py"
diff --git a/examples/huawei_2017/test_scoring.py b/examples/huawei_2017/test_scoring.py
new file mode 100644
index 00000000..b2111ebd
--- /dev/null
+++ b/examples/huawei_2017/test_scoring.py
@@ -0,0 +1,66 @@
+"""
+Test script to demonstrate the new scoring system
+"""
+import math
+
+def calculate_scores(avg_time_seconds):
+    """Calculate time scores for different execution times"""
+    time_score = math.exp(-avg_time_seconds / 10.0)
+
+    if avg_time_seconds < 1.0:
+        time_score = min(1.0, time_score * 1.2)
+
+    return time_score
+
+print("=" * 70)
+print("HUAWEI 2017 CDN - NEW SCORING SYSTEM")
+print("=" * 70)
+print("\nScoring Weights:")
+print("  - Success Rate: 40%")
+print("  - Cost Score:   35%")
+print("  - Time Score:   25%")
+print("\nFormula: combined_score = 0.40 × success_rate + 0.35 × cost_score + 0.25 × time_score")
+print("\n" + "=" * 70)
+print("TIME SCORE TABLE (exponential decay)")
+print("=" * 70)
+print(f"{'Avg Time':<12} {'Time Score':<12} {'Impact on Total':<20} {'Assessment'}")
+print("-" * 70)
+
+time_examples = [0.5, 1.0, 2.0, 3.0, 5.0, 7.0, 10.0, 15.0, 20.0, 25.0, 30.0]
+
+for t in time_examples:
+    time_score = calculate_scores(t)
+    impact = time_score * 0.25  # 25% weight
+
+    if t <= 3:
+        assessment = "Excellent"
+    elif t <= 7:
+        assessment = "Good"
+    elif t <= 15:
+        assessment = "Acceptable"
+    elif t <= 25:
+        assessment = "Poor"
+    else:
+        assessment = "Very Poor"
+
+    print(f"{t:<12.1f}s {time_score:<12.4f} {impact:<20.4f} {assessment}")
+
+print("\n" + "=" * 70)
+print("COMBINED SCORE EXAMPLES")
+print("=" * 70)
+print("Assuming 100% success rate and perfect cost score (1.0):\n")
+
+for t in [1, 5, 10, 20, 30]:
+    time_score = calculate_scores(t)
+    combined = 0.40 * 1.0 + 0.35 * 1.0 + 0.25 * time_score
+    print(f"  Time {t:2d}s → combined_score = {combined:.4f}")
+
+print("\n" + "=" * 70)
+print("KEY INSIGHTS:")
+print("=" * 70)
+print("1. Time matters significantly (25% weight)!")
+print("2. Going from 5s to 10s loses 0.06 points (~6% of total score)")
+print("3. Going from 10s to 20s loses 0.06 points (~6% of total score)")
+print("4. Solutions slower than 20s are heavily penalized")
+print("5. Target execution time: < 5 seconds per test case")
+print("=" * 70)
diff --git a/openevolve/config.py b/openevolve/config.py
index 7cc540b4..724b003d 100644
--- a/openevolve/config.py
+++ b/openevolve/config.py
@@ -365,7 +365,7 @@ class Config:
     @classmethod
     def from_yaml(cls, path: Union[str, Path]) -> "Config":
         """Load configuration from a YAML file"""
-        with open(path, "r") as f:
+        with open(path, "r",encoding='utf-8') as f:
             config_dict = yaml.safe_load(f)
         return cls.from_dict(config_dict)
 
diff --git a/openevolve/controller.py b/openevolve/controller.py
index 4f27f043..04f79bc1 100644
--- a/openevolve/controller.py
+++ b/openevolve/controller.py
@@ -221,7 +221,7 @@ def _setup_logging(self) -> None:
 
     def _load_initial_program(self) -> str:
         """Load the initial program from file"""
-        with open(self.initial_program_path, "r") as f:
+        with open(self.initial_program_path, "r",encoding='utf-8') as f:
             return f.read()
 
     async def run(
@@ -291,7 +291,7 @@ async def run(
                 if numeric_metrics:
                     avg_score = sum(numeric_metrics) / len(numeric_metrics)
                     logger.warning(
-                        f"⚠️  No 'combined_score' metric found in evaluation results. "
+                        f"No 'combined_score' metric found in evaluation results. "
                         f"Using average of all numeric metrics ({avg_score:.4f}) for evolution guidance. "
                         f"For better evolution results, please modify your evaluator to return a 'combined_score' "
                         f"metric that properly weights different aspects of program performance."
diff --git a/openevolve/database.py b/openevolve/database.py
index 1c7f7393..b72baf36 100644
--- a/openevolve/database.py
+++ b/openevolve/database.py
@@ -1036,6 +1036,16 @@ def _is_novel(self, program_id: int, island_idx: int) -> bool:
 
         program = self.programs[program_id]
         embd = self.embedding_client.get_embedding(program.code)
+
+        if embd is None:
+            logger.warning(
+                "Embedding unavailable for program %s; skipping similarity check "
+                "and treating program as novel.",
+                program.id,
+            )
+            self.programs[program_id].embedding = None
+            return True
+
         self.programs[program_id].embedding = embd
         
         max_smlty = float('-inf')
diff --git a/openevolve/embedding.py b/openevolve/embedding.py
index 74d3c57a..36762edd 100644
--- a/openevolve/embedding.py
+++ b/openevolve/embedding.py
@@ -4,9 +4,10 @@
 """
 
 import os
-import openai
-from typing import Union, List
 import logging
+from typing import List, Optional, Union
+
+import openai
 
 logger = logging.getLogger(__name__)
 
@@ -28,8 +29,7 @@
 }
 
 class EmbeddingClient:
-    def __init__(
-        self, model_name: str = "text-embedding-3-small"):
+    def __init__(self, model_name: str = "text-embedding-3-small"):
         """
         Initialize the EmbeddingClient.
 
@@ -37,6 +37,7 @@ def __init__(
             model (str): The OpenAI embedding model name to use.
         """
         self.client, self.model = self._get_client_model(model_name)
+        self._disabled_reason: Optional[str] = None
     
     def _get_client_model(self, model_name: str) -> tuple[openai.OpenAI, str]:
         if model_name in OPENAI_EMBEDDING_MODELS:
@@ -57,7 +58,7 @@ def _get_client_model(self, model_name: str) -> tuple[openai.OpenAI, str]:
 
     def get_embedding(
         self, code: Union[str, List[str]]
-    ) -> Union[List[float], List[List[float]]]:
+    ) -> Optional[Union[List[float], List[List[float]]]]:
         """
         Computes the text embedding for a code string.
 
@@ -69,6 +70,13 @@ def get_embedding(
             list: Embedding vector for the code or None if an error
                 occurs.
         """
+        if self._disabled_reason:
+            logger.debug(
+                "Embedding client disabled; reusing cached reason: %s",
+                self._disabled_reason,
+            )
+            return None
+
         if isinstance(code, str):
             code = [code]
             single_code = True
@@ -83,9 +91,17 @@ def get_embedding(
                 return response.data[0].embedding
             else:
                 return [d.embedding for d in response.data]
-        except Exception as e:
-            logger.info(f"Error getting embedding: {e}")
-            if single_code:
-                return [], 0.0
-            else:
-                return [[]], 0.0
+        except Exception as e:  # pragma: no cover - depends on external API
+            error_message = str(e)
+            logger.error("Embedding request failed: %s", error_message)
+
+            if "unsupported_country_region_territory" in error_message:
+                self._disabled_reason = (
+                    "OpenAI embeddings endpoint is unavailable from this region."
+                )
+                logger.error(
+                    "Disabling embedding client after unsupported region response; "
+                    "set a supported embedding provider or disable novelty checks."
+                )
+
+            return None
diff --git a/openevolve/llm/openai.py b/openevolve/llm/openai.py
index e38bc2f4..f08b4343 100644
--- a/openevolve/llm/openai.py
+++ b/openevolve/llm/openai.py
@@ -133,6 +133,15 @@ async def generate_with_context(
             if reasoning_effort is not None:
                 params["reasoning_effort"] = reasoning_effort
 
+        # ModelScope Qwen models require enable_thinking=false for non-streaming requests
+        if (
+            isinstance(self.api_base, str)
+            and "api-inference.modelscope.cn" in self.api_base
+        ):
+            extra_body = kwargs.get("extra_body") or {}
+            extra_body.setdefault("enable_thinking", False)
+            params["extra_body"] = extra_body
+
         # Add seed parameter for reproducibility if configured
         # Skip seed parameter for Google AI Studio endpoint as it doesn't support it
         seed = kwargs.get("seed", self.random_seed)
diff --git a/openevolve/process_parallel.py b/openevolve/process_parallel.py
index 144449d9..8ee7b5b7 100644
--- a/openevolve/process_parallel.py
+++ b/openevolve/process_parallel.py
@@ -561,17 +561,17 @@ async def run_evolution(
                         ):
                             avg_score = safe_numeric_average(child_program.metrics)
                             logger.warning(
-                                f"⚠️  No 'combined_score' metric found in evaluation results. "
+                                "No 'combined_score' metric found in evaluation results. "
                                 f"Using average of all numeric metrics ({avg_score:.4f}) for evolution guidance. "
-                                f"For better evolution results, please modify your evaluator to return a 'combined_score' "
-                                f"metric that properly weights different aspects of program performance."
+                                "For better evolution results, please modify your evaluator to return a 'combined_score' "
+                                "metric that properly weights different aspects of program performance."
                             )
                             self._warned_about_combined_score = True
 
                     # Check for new best
                     if self.database.best_program_id == child_program.id:
                         logger.info(
-                            f"🌟 New best solution found at iteration {completed_iteration}: "
+                            f"New best solution found at iteration {completed_iteration}: "
                             f"{child_program.id}"
                         )
 
diff --git a/openevolve/prompt/templates.py b/openevolve/prompt/templates.py
index 465f92d7..7b62f6ca 100644
--- a/openevolve/prompt/templates.py
+++ b/openevolve/prompt/templates.py
@@ -202,7 +202,7 @@ def _load_from_directory(self, directory: Path) -> None:
         # Load fragments.json if exists
         fragments_file = directory / "fragments.json"
         if fragments_file.exists():
-            with open(fragments_file, "r") as f:
+            with open(fragments_file, "r",encoding='utf-8') as f:
                 loaded_fragments = json.load(f)
                 self.fragments.update(loaded_fragments)