Genetic-MiniMax

Approximating Fourier Coefficients Using Genetic Algorithm + Implementing Pentago game with MiniMax Algorithm

🧬 Part 1: Approximating Fourier Coefficients Using Genetic Algorithm

Objective:
Estimate the Fourier series coefficients of an unknown function using a genetic algorithm.

Preview of one of the comparison plots:

🎮 Part 2: Implementing Pentago with the Minimax Algorithm

Objective:
Develop a playable Pentago game with an AI opponent powered by the Minimax algorithm.

Game Description:
Pentago is a two-player abstract strategy game played on a 6×6 board divided into four 3×3 quadrants.
Players take turns placing a marble and then rotating one of the quadrants 90° in either direction.
The goal is to align five of your marbles in a row—horizontally, vertically, or diagonally.

Minimax Algorithm:

• Uses depth-limited search for AI decision-making
• Integrates Alpha-Beta pruning to improve efficiency
• AI difficulty can be adjusted by changing the depth parameter
  (Note: Higher depth = stronger AI but slower computation)

Results:

• Demonstrated the effectiveness of Alpha-Beta pruning through simulations
• Created a fully playable Pentago game featuring a human vs. AI opponent
• Preview shown below:

Red: Human Agent & Blue: AI