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Hi, I tested the pygad library version 2.17.0 for solving the knapsack problem and I observed that even if I set allow_duplicate_genes=False, I still have duplicated genes in the solution.
As shown in the following solution, It can be seen that gene -5 is duplicated, -25 also.
[28, -3, 12, -21, 18, 24, -22, 24, -30, 2, 28, -27, 21, -5, -5, 5, -1, 7, 12, 23, -9, -15, -21, -16, -16, -23, 26, -14, -25, -25]
I instantiate pygad as following
ga_instance = pygad.GA(num_generations=METAHEURISTIC_NO_OF_GENERATIONS,
num_parents_mating=METAHEURISTIC_NUM_PARENS_MATING,
gene_type=int,
fitness_func=fitness_func,
sol_per_pop=METAHEURISTIC_POPULATION_SIZE,
num_genes=METAHEURISTIC_GENE_LENGTH,
init_range_low=METAHEURISTIC_GENE_LOW,
init_range_high=METAHEURISTIC_GENE_HIGH,
parent_selection_type=METAHEURISTIC_SELECTION_TYPE,
keep_parents=METAHEURISTIC_KEEP_PARENTS,
crossover_type=METAHEURISTIC_CROSSOVER_TYPE,
crossover_probability=METAHEURISTIC_CROSSOVER_RATE,
mutation_type=METAHEURISTIC_MUTATION_TYPE,
mutation_probability = METAHEURISTIC_MUTATION_RATE,
# mutation_probability = METAHEURISTIC_MUTATION_PROBABILITY,
#mutation_percent_genes=METAHEURISTIC_MUTATION_PERCENT_GENES,
stop_criteria="saturate_75",
allow_duplicate_genes=False,
parallel_processing=['process', 10]
)and the fitness function is defined as:
def fitness_func(solution, solution_idx):
# print(solution)
calculated_mass = 0
calculated_value = 0
output = 0
unique_elements = check_duplicates(solution)
if len(unique_elements) < len(solution):
return 1/sys.maxsize
for gene in solution:
position = abs(gene) - 1
if gene > 0:
calculated_mass += items[position].m
calculated_value += items[position].v
output = fitness_function(calculated_mass, calculated_value)
if calculated_mass > MAX_ALLOWED_MASS:
return 1/sys.maxsize
return output