Genetic Algorithm for Optimal Trajectory Planning

This repository implements a genetic algorithm (GA) to optimize trajectory planning using control parameters (gamma and beta). The algorithm evolves these parameters over multiple generations to find an optimal control strategy for a given dynamic system.

Features

• Binary Encoding & Gray Code Conversion: Parameters are encoded in binary and converted to Gray code for efficient crossover.
• Fitness Evaluation via an ODE System: The fitness function is computed by simulating trajectories using Euler's method.
• Genetic Operators: Selection, crossover, and mutation are applied to evolve the control parameters.
• Visualization: Generates trajectory plots and evolution graphs for control parameters.

Installation

Usage

Run the main script to execute the genetic algorithm:

python source_code.py

This will simulate the evolutionary process and output the optimized trajectory.

Configuration

You can modify key parameters in the script:

• POP_SIZE: Population size
• MAX_GEN: Maximum number of generations
• MUTATION_RATE: Mutation probability
• MAX_TIME: Time constraint for execution

Output

• Trajectory Plots: Shows the evolution of the optimal trajectory.
• Parameter Evolution Graphs: Visualizes how gamma and beta change over generations.

Contributing

Feel free to submit issues or pull requests to improve this project!

License

This project is licensed under the MIT License.