GAs operate by mimicking the process of evolution. The algorithm starts with a population of potential solutions, known as individuals. Each individual is represented by a chromosome, which encodes the parameters of the solution. Through a series of operationsâselection, crossover, and mutationâthe algorithm iteratively evolves the population towards better solutions.
1. Selection: The fittest individuals are chosen based on a fitness function, which measures how well each individual performs in the given task. 2. Crossover: Pairs of selected individuals exchange segments of their chromosomes to create offspring, combining the strengths of both parents. 3. Mutation: Random changes are introduced to some individuals to maintain genetic diversity and explore new solutions.
