vmeirelle / Genetic-Algorithm

A basic genetic algorithm implementation for finding maximum, minimum, and zeros of a given function.

Genetic Algorithm Example

This code represents my first foray into the world of programming during my studies as a Civil Engineering student at UFOB (Federal University of Western Bahia). The primary goal was to explore genetic algorithms and their application in optimizing structural designs

Getting Started

1. Prerequisites:
   • Python 3.x installed on your system.
   • Basic understanding of genetic algorithms.
2. Installation:
   • Clone this repository to your local machine.
   • Open a terminal and navigate to the project directory.
3. Usage:
   • Run the genetic_algorithm.py script.
   • Follow the prompts to set the number of generations, gene quantities, and chromosome quantities.
   • Observe the algorithm’s progress as it evolves the population.

Algorithm Overview

1. Initialization:
   • Create an initial population of random binary chromosomes.
   • Evaluate each chromosome’s fitness using the given function.
2. Selection:
   • Select the best-performing chromosomes (based on fitness) to form the next generation.
3. Crossover:
   • Combine pairs of selected chromosomes to create offspring.
   • Perform crossover (exchange of genetic material) to create new chromosomes.
4. Mutation:
   • Introduce random mutations (flips) in the offspring’s genes.
5. Repeat:
   • Repeat steps 2-4 for a specified number of generations.
6. Results:
   • The algorithm converges toward an optimal solution (maximizing the given function).

Example Output

Choose the number of generations: 50
Choose the gene quantities in each chromosome: 10
Choose the chromosome quantities in the population: 20

Generation: 1 /// Population Values: [123, 45, ...]
...
Generation: 50 /// Population Values: [456, 789, ...]