Genetic Algorithm vs Brute Force Comparison

Project overview

This project, developed as part of my gymnasium (high school) studies, demonstrates a comparison between a Genetic Algorithm (GA) and a Brute Force method. The primary aim is to analyze and compare the efficiency and effectiveness of these two approaches in solving optimization problems, such as the Traveling Salesman Problem.

Features

• Implementation of a Genetic Algorithm for optimization.
• Brute Force approach for comparison.
• Performance analysis between the two methods.
• Use of high-resolution timer for accurate performance measurement.

Requirements

• C++ Compiler (e.g., GCC, Clang, MSVC)
• C++ Standard: C++11 or later (due to usage of and other features)

Installation

Clone the repository to your local machine:

git clone https://github.com/SimonNyvall/Traveling-Salesman-AI.git

Usage

Compile the C++ file using your preferred C++ compiler. For example, using g++:

g++ -o ga_comparison AI/SalemanAI.cpp

Run the compiled program:

./ga_comparison

Implementation Details

• Genetic Algorithm: The GA is implemented to optimize the solution by evolving over generations.
• Brute Force Method: A straightforward approach is implemented for comparison.
• Timer Class: Used for measuring the execution time of both methods.
• Population and City Initialization: Sets up the initial state for the GA.
• Mutation and Fitness Calculation: Core components of the GA for evolving solutions.