GeneticAlgorithm is a generic implementation that evolves a population of Chromosome values. Each generation evaluates fitness, selects parents, creates children through crossover, mutates individuals, and replaces the population until a threshold or generation limit is reached. section 5.3
Configure the algorithm with:
- A fitness threshold and maximum generation count. section 5.3
- Mutation and crossover probabilities. section 5.3
- Roulette-wheel or tournament selection. section 5.3
A configured run
An initializer can create a population with a target threshold, run for at most 100 generations, mutate each individual with probability 0.01, cross children with probability 0.7, and use four-person tournaments by default.
