Chess AI Game with Genetic Algorithm and GUI

Overview

This project is a Python-based chess game that combines Artificial Intelligence (AI) with an intuitive Graphical User Interface (GUI). The AI opponent is powered by a Genetic Algorithm, making gameplay challenging and engaging. The game allows human players to compete against a smart AI while adhering to chess rules and providing a realistic gaming experience.

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Features

• Graphical User Interface (GUI):

  • A visually appealing and interactive chessboard for user interaction.
  • Highlights possible moves and visually updates the board in real-time.
  • Alerts for game events such as check, checkmate, stalemate, or invalid moves.
  • Displays endgame messages (e.g., "Checkmate!").

• AI Opponent:

  • Built using a Genetic Algorithm to evaluate and select the best moves.
  • Optimizes gameplay by analyzing potential moves and simulating outcomes.
  • Implements Minimax and crossover techniques for strategic decision-making.

• Chess Mechanics:

  • Enforces official chess rules, including legal moves, checks, checkmate, and stalemates.
  • Handles turn-based gameplay between the human player and AI.

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Problem Description

Chess, while simple in rules, is a complex game requiring deep strategic and tactical thinking. Many beginners struggle against skilled players due to the game's intricacies. This project addresses this challenge by creating an AI-driven chess program that provides a smooth and engaging experience for users. The AI uses a Genetic Algorithm to simulate a challenging opponent, making it enjoyable for both beginners and experienced players.

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Program Functionality

Inputs:

1. Player Moves: The user interacts with the GUI to make chess moves.
2. AI Parameters: Internal configurations like the number of generations, population size, mutation rate, and sequence length for the Genetic Algorithm.

Outputs:

1. Real-time Visual Updates: The board and pieces update instantly after each move.
2. Notifications: Alerts for invalid moves, check, checkmate, or stalemate.
3. AI Moves: The program computes and displays the AI’s move using the Genetic Algorithm.
4. Endgame Messages: Displays the result, such as "Checkmate!" or "Player Wins!".

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Implementation

1. Libraries Used

• customtkinter: For creating the interactive and customizable GUI.
• python-chess: For chessboard representation, legal move validation, and game state management.
• Pillow: For handling chess piece images.
• random: For generating random sequences in the Genetic Algorithm.

2. Genetic Algorithm

The Genetic Algorithm powers the AI opponent and evaluates the board to determine the best possible moves. Key methods include:

• evaluate_fitness: Calculates the fitness score of each move based on piece safety and board position.
• genetic_algorithm: Simulates evolution to find the optimal sequence of moves by:
  • Initializing random populations of moves.
  • Evaluating fitness for each sequence.
  • Using crossover and mutation to generate new populations.
  • Selecting the best move sequence.
• crossover_sequences: Combines two strong sequences to create better strategies.
• mutate_sequence: Introduces randomness to explore new tactical opportunities.

3. Graphical User Interface (GUI)

The GUI is designed to ensure a seamless and intuitive user experience. Key features include:

• Chessboard Display: Shows the board with properly placed pieces.
• Move Handling: Validates and executes player moves, highlights possible moves, and sends updates to the AI.
• Game Over Screen: Displays the result (e.g., "Checkmate!") and disables further interactions.
• AI Integration: Automatically computes and executes AI moves during its turn.

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How to Run the Program

1. Install the required libraries:

   pip install customtkinter python-chess Pillow