Genetic Algorithm Project

This project implements a simple genetic algorithm with the following features:

• Binary and real encoding for individuals
• Configurable hyperparameters (population size, generations, etc.)
• Multiple selection methods: elitist, roulette, ranking (v1/v2), tournament, steady-state, crowding factor
• Multiple crossover methods: uniform, arithmetic, flat, BLX-α, PMX (for permutations)
• Multiple mutation methods: random, uniform, non-uniform, swap
• Fitness function for n alleles
• Generation results table (Individual, Mapping, DNA, Fitness)
• Visualization: best and average fitness per generation

Usage

1. Configure parameters in main.py.
2. Run the project: python main.py
3. View results and evolution graphs.

Structure

• src/individual.py: Individual representation
• src/population.py: Population management
• src/selection.py: Selection methods
• src/crossover.py: Crossover methods
• src/mutation.py: Mutation methods
• src/fitness.py: Fitness function
• src/visualization.py: Plotting results
• src/main.py: Main script

Requirements

• Python 3.8+
• matplotlib
• numpy

Install dependencies:

pip install matplotlib numpy