This Python script demonstrates a simple genetic algorithm (GA) that evolves a population of chromosomes to match a target chromosome defined by TARGET_CHROMOSOME.
This Python script demonstrates a simple genetic algorithm (GA) that evolves a population of chromosomes to match a target chromosome defined by TARGET_CHROMOSOME.
- Population Size (POPULATION_SIZE): The size of the population.
- Number of Elite Chromosomes (NUMB_OF_ELITE_CHROMOSOMES): Number of elite chromosomes to retain in each generation.
- Tournament Selection Size (TOURNAMENT_SELECTION_SIZE): Number of chromosomes participating in tournament selection.
- Target Chromosome (TARGET_CHROMOSOME): The desired chromosome for evolution.
- Mutation Rate (MUTATION_RATE): Probability of gene mutation in each chromosome.
- Represents an individual chromosome in the population.
- Initializes a random chromosome with genes (0s and 1s).
get_genes(): Returns the genes of the chromosome.get_fitness(): Calculates the fitness by counting matching genes with the target chromosome.__str__(): Returns the genes as a string.
- Manages a population of chromosomes.
- Initializes the population with random chromosomes.
- Contains main GA operations.
evolve(pop): Evolves the population through crossover and mutation._crossover_population(pop): Creates a new population through crossover on selected chromosomes._mutate_population(pop): Mutates genes based on the mutation rate._crossover_chromosomes(chromosomes1, chromosomes2): Performs crossover between two chromosomes._mutate_chromosome(chromosome): Mutates the genes in a chromosome._select_tournament_population(pop): Selects a subset of the population for tournament selection.print_the_population(pop, gen_number): Prints population information.
- Clone the project.
- Run
main.pyto start the genetic algorithm.
The algorithm iteratively evolves the population until the fittest chromosome matches the target chromosome.
This project is licensed under the [License Name]. See the LICENSE file for more details.