GeneticAlgorithm(
    initialization_strategy::P,
    fitness_function::Function,
    selection_strategy::S,
    crossover_method::C,
    mutation_method::M;
    elitism::Bool=true,
    verbose::Bool=false,
    max_generations::Int=5,
    mutation_rate::Float64=0.1,
    save_best::Bool=false,
) where {P<:PopulationInitializationMethod,S<:SelectionMethod,C<:CrossoverMethod,M<:MutationMethod}

Defines a genetic algorithm with the specified parameters for selection, mutation, crossover and fitness evaluation.

Arguments

• initialization_strategy::P: Strategy to initialize the population.
• fitness_function::Function: Function to calculate the fitness of each chromosome.
• selection_strategy::S: Strategy to select parents for crossover.
• crossover_method::C: Method to generate offspring from selected parents.
• mutation_method::M: Method to mutate the offspring.
• elitism::Bool=true: If true, the best individual from the previous generation is carried over to the next generation.
• verbose::Bool=false: If true, additional information is printed during the execution.
• max_generations::Int64=5: Number of generations the algorithm runs for.
• mutation_rate::Float64=0.1: Probability of mutation for each offspring in a generation.
• save_best::Bool=false: If true, the best chromosomes and their fitness scores are saved for visualization.

Fields

• initialization_strategy::P: Strategy to initialize the population.
• fitness_function::Function: Function to calculate the fitness of each chromosome.
• max_generations::Int64: Number of generations the algorithm runs for.
• selection_strategy::S: Strategy to select parents for crossover.
• crossover_method::C: Method to generate offspring from selected parents.
• mutation_method::M: Method to mutate the offspring.
• mutation_rate::Float64: Probability of mutation.
• elitism::Bool: If true, the best individual from the previous generation is carried over to the next generation.
• verbose::Bool: If true, additional information is printed during the execution.
• save_best::Bool: If true, the best chromosomes and their fitness scores are saved for visualization.
• best_chromosomes::Vector{Chromosome}: Stores the best chromosomes for each generation.
• best_fitness::Vector{Float64}: Stores the fitness scores of the best chromosomes for each generation.

Note that the best_chromosomes and best_fitness fields store max_generations + 1 values, including the initial population.

optimize(genetic_algorithm::GeneticAlgorithm)::Chromosome

Runs the genetic algorithm optimization loop and returns the best Chromosome.

genetic_algorithm specifies the configuration of the genetic algorithm that is optimized. If the field genetic_algorithm.save_best is true the field genetic_algorithm.best_chromosomes and genetic_algorithm.best_fitness are filled during the optimization process.