Implementation and Results of a "Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem"

1. Introduction

In this archive, we provide the implementation and experimental results of a Randomized Local Search (RLS) with and without FFA applied to the Quadratic Assignment Problem. These are the data used in the paper below, which contains the exact specification of all algorithms, objective functions, and the encoding we applied.

Jiayang Chen, Zhize Wu, Sarah L. Thomson, and Thomas Weise. Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem. In 16th International Conference on Evolutionary Computation Theory and Applications (ECTA'24), part of the 16th International Joint Conference on Computational Intelligence (IJCCI'24). November 20-24, 2024. Porto, Portugal. Setúbal, Portugal: SciTePress.

To run the experiments, you need moptipyapps and moptipy, which contain the actual algorithm implementations. Both packages are available on GitHub and on PyPI. However, we include several versions of them in the folder source/packages, just in case.

2. Directory Structure

This archive contains the following directories:

source contains the Python source codes needed to run the experiment.

source/packages contains the source codes of the Python packages with the actual algorithm implementations.

source/experiment_execution_scripts contains the scripts to run the experiments.

results is the directory with the results, i.e., with the log files generated by the experiment.

evaluator is the folder containing Python scripts that were used to evaluate these results.

evaluation was generated using the evaluation scripts and contains tables and figures and a result summary in CSV format.

3. License

The files in this repository are under the Creative Commons Attribution 4.0 International, with the exception of the benchmark datasets included, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file LICENSE.txt in this archive.

4. Contact

If you have any questions or suggestions, please contact

Mr. Jiayang CHEN (陈嘉阳) of the Institute of Applied Optimization (应用优化研究所, IAO) of the School of Artificial Intelligence and Big Data (人工智能与大数据学院) at Hefei University (合肥大学) in Hefei, Anhui, China (中国安徽省合肥市) via email to [e-mail].