Boosting GA Performance: A Fuzzy Approach to Uncertainty Issues Involving Parameters in Genetic Algorithms

João Ferro, José Brito, Robério Santos, Roberta Lopes, Evandro Costa

2024

Abstract

This article addresses issues involving two sources of uncertainty in the stochastic search problem based on a genetic algorithm approach. We improve the mutation rate parameter by fuzzifying the population diversity and the individual adaptation value. A relevant aspect of this investment is related to the fact that this parameter, which presents uncertainty of the possibilistic type, directly interferes with the uncertainty of the probabilistic type of the genetic algorithm and also in the convergence and quality of the solution found by the genetic algorithm. Moreover, in parallel, we improve the understanding behavior of selection and replacement methods. Experiments were carried out on the case study with the classic OneMax problem to evaluate the performance of the proposed solution, analyzing aspects such as the convergence time, the quality of the solution, and the diversity of the population. The results obtained through the treatment of uncertainty and its impacts are presented in this article, showing relevant performance for the proposed algorithm, with the respective treatment of uncertainties.

Paper Citation

in Harvard Style

Ferro J., Brito J., Santos R., Lopes R. and Costa E. (2024). Boosting GA Performance: A Fuzzy Approach to Uncertainty Issues Involving Parameters in Genetic Algorithms. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 750-757. DOI: 10.5220/0012389200003636

in Bibtex Style

@conference{icaart24,
author={João Ferro and José Brito and Robério Santos and Roberta Lopes and Evandro Costa},
title={Boosting GA Performance: A Fuzzy Approach to Uncertainty Issues Involving Parameters in Genetic Algorithms},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={750-757},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012389200003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Boosting GA Performance: A Fuzzy Approach to Uncertainty Issues Involving Parameters in Genetic Algorithms
SN - 978-989-758-680-4
AU - Ferro J.
AU - Brito J.
AU - Santos R.
AU - Lopes R.
AU - Costa E.
PY - 2024
SP - 750
EP - 757
DO - 10.5220/0012389200003636
PB - SciTePress