Genetic Operators
Genetic operators are the building blocks of genetic algorithms, which are optimization algorithms inspired by the process of natural selection. These operators mimic the biological processes of reproduction, mutation, and recombination to generate new candidate solutions from the existing ones. Reproduction involves selecting the fittest individuals from the population to pass on their genetic material to the next generation. Mutation introduces random changes in the genetic material to explore new regions of the search space. Recombination combines the genetic material of two or more individuals to create new offspring with a mix of their traits. These operators are applied iteratively to generate a sequence of candidate solutions that converge towards the optimal solution. The choice of genetic operators and their parameters can significantly affect the performance of the genetic algorithm.
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