Genetic Algorithm Optimization

This project implements a Genetic Algorithm (GA) in Python, designed for solving optimization problems. The GA is capable of minimizing multi-dimensional functions through evolutionary techniques inspired by biological processes.

This is a personal project developed for educational purposes. For more mature and robust implementations of GA, check out:

• PyGAD
• DEAP

Features

• Crossover: Combines pairs of individuals to produce offspring, ensuring diversity and exploration of the solution space.
• Mutation: Introduces random changes in the offspring, aiding in diversity and preventing premature convergence.
• Tournament Selection: Selects the best candidates as parents for the next generation, based on their fitness.
• GA Minimize Function: Orchestrates the GA process to minimize a given multi-dimensional function.

Getting Started

Prerequisites

• Python 3.x
• NumPy
• (Optional) Matplotlib for any graphical representation of results

Installation

1. Clone the repository:

   git clone https://github.com/jhklarcher/genetic-algorithm.git

2. Navigate to the cloned directory.
3. Install the package:

   pip install .

Usage

1. Import the GA function:

   from genetic_algorithm import ga_minimize

2. Define your optimization problem as a function.
3. Call ga_minimize with your function and other GA parameters.

Examples

See the examples directory for some usage examples.

License

Distributed under the MIT License. See LICENSE for more information.