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Genetic Algorithm and its practicality in Machine Learning
What is GA and how to use it to train Machine Learning models?
Introduction
Genetic Algorithm (GA) is a type of natural computing algorithm, which are algorithms developed to try to solve problems by replicating phenomena and behaviors present in nature.
There are several natural algorithms in the literature, such as Particle Swarm Optimization (PSO), which tries to mathematically simulate the social behavior of schools of fish or flocks of birds, and Ant Colony Optimization (ACO), which seeks to find the best paths in a graph simulating the behavior of ants when moving.
These types of algorithms are used to solve optimization problems, where you want to find optimal values (maximum or minimum depending on the problem analyzed) within a search space. One of the most studied optimization problems is the so-called traveling salesman problem.
Among all Natural Computing algorithms, one of the most famous is the Genetic Algorithm.
