This repository contains Python code implementing a genetic algorithm for function optimization. The algorithm is an academic example of the genetic algorithms usage designed to find the maximum value of a quadratic function within a given domain.
- Genetic Algorithm: Utilizes a genetic algorithm approach to evolve a population of chromosomes towards the optimal solution.
- Function Optimization: Seeks to find the maximum value of a quadratic function within a specified domain.
- Selection, Crossover, Mutation: Implements selection, crossover, and mutation operations to iteratively improve the population.
- Fitness Evaluation: Evaluates the fitness of each chromosome based on the quadratic function.
- Visualization: Provides visualization of the evolution process, showing the mean fitness and the maximum fitness achieved over generations.
- Python 3.x
- Matplotlib
-
Clone the repository:
git clone https://github.com/yourusername/genetic-algorithm.git -
Navigate to the project directory:
cd genetic-algorithm -
Ensure your input file (
input.txt) containing algorithm parameters and domain specifications is in the root directory. The input file should have the following format:- Number of chromosomes
- Domain of the function (closed interval endpoints)
- Coefficients of the quadratic function
- Precision for discretizing the interval
- Crossover probability
- Mutation probability
- Number of generations
Refer to the provided
input.txtfile for an example. -
Run the Python script:
python genetic_algorithm.py -
Check the output file (
output.txt) for the results, including initial and final populations, as well as statistics on the evolution process.
The algorithm generates an output file (output.txt) containing:
- Initial population
- Selection probabilities for the initial population
- Chromosomes selected for crossover
- Chromosomes after crossover
- Chromosomes selected for mutation
- Chromosomes after mutation
- Statistics on mean fitness and maximum fitness over generations