/Suduko-Solver-Using-Genetic-Algorithm

Implementation of genetic algorithms for solving Sudoku, CA2, Artificial Intelligence Course (Fall 2021), University of Tehran

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Suduko-Solver-Using-Genetic-Algorithms

This project centers around the application of a Genetic Algorithm (GA) to solve Sudoku puzzles.

What is Genetic Algorithm?

Genetic Algorithm (GA) serves as a search technique in computing, aimed at finding true or approximate solutions to optimization and search problems. The core principle is to emulate natural selection, constructing a population of candidate solutions. The primary focus lies in evolving a diverse population through mutation and crossover, yielding robust candidate solutions.

Objectives

We aim to achieve the following objectives in this project:

  1. Defining Genetic Algorithm Terms:

    • Establishing clear definitions for crucial terms such as Gene and Chromosome.

  2. Initial Population Construction:

    • Forming the initial population of candidate solutions for the genetic algorithm.

  3. Fitness Function:

    • Defining and implementing a Fitness Function to evaluate the fitness of potential solutions.

  4. Crossover and Mutation:

    • Implementing the processes of Crossover and Mutation, vital to the genetic algorithm's evolution and convergence.

  5. Algorithm Execution:

    • Running the genetic algorithm to effectively solve Sudoku puzzles.

By achieving these goals, we seek to showcase the potential and effectiveness of Genetic Algorithms in solving Sudoku puzzles, highlighting their application in optimization and search problems.