https://github.com/renatomaynard/a-multiple-population-coarse-grained-genetic-algorithm-to-solve-the-quadratic-assignment-problem-
A Multiple-population coarse-grained Genetic Algorithm to solve the Quadratic Assignment Problem
https://github.com/renatomaynard/a-multiple-population-coarse-grained-genetic-algorithm-to-solve-the-quadratic-assignment-problem-
c cuda genetic-algorithm quadratic-assignment-problem
Last synced: 2 months ago
JSON representation
A Multiple-population coarse-grained Genetic Algorithm to solve the Quadratic Assignment Problem
- Host: GitHub
- URL: https://github.com/renatomaynard/a-multiple-population-coarse-grained-genetic-algorithm-to-solve-the-quadratic-assignment-problem-
- Owner: RenatoMaynard
- License: mit
- Created: 2024-12-26T23:30:42.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-03-14T00:46:48.000Z (2 months ago)
- Last Synced: 2025-03-14T01:43:39.493Z (2 months ago)
- Topics: c, cuda, genetic-algorithm, quadratic-assignment-problem
- Language: Jupyter Notebook
- Homepage:
- Size: 303 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Genetic Algorithm (GA) Implementations for the Quadratic Assignment Problem (QAP)
## Overview
This repository contains two implementations of Genetic Algorithms (GAs) for solving the Quadratic Assignment Problem (QAP):
1. **GA implemented in C**: A sequential version of the Genetic Algorithm designed to run on CPU.
2. **GA implemented in CUDA**: A parallelized version of the Genetic Algorithm GPU for improved computational performance, developed using the paper ["Solving Quadratic Assignment Problems by Genetic
Algorithms with GPU Computation: A Case Study"](https://dl.acm.org/doi/10.1145/1570256.1570355) as a reference.
3. **Mathematical Model in Jupyter Notebook**: A Jupyter Notebook containing the mathematical model to solve the QAP.
## Acknowledgments
The instances used in this project are sourced from [QAPLIB](https://coral.ise.lehigh.edu/data-sets/qaplib/), a benchmark library for Quadratic Assignment Problems. Special thanks to the authors of the paper ["Solving Quadratic Assignment Problems by Genetic
Algorithms with GPU Computation: A Case Study"](https://dl.acm.org/doi/10.1145/1570256.1570355).