In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection. Five phases are considered in a genetic algorithm.

  1. Initial population
  2. Fitness function
  3. Selection
  4. Crossover
  5. Mutation
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