Optimizing Genetic Algorithms Using the Binomial Distribution

Vincent A Cicirello

2024

Abstract

Evolutionary algorithms rely very heavily on randomized behavior. Execution speed, therefore, depends strongly on how we implement randomness, such as our choice of pseudorandom number generator, or the algorithms used to map pseudorandom values to specific intervals or distributions. In this paper, we observe that the standard bit-flip mutation of a genetic algorithm (GA), uniform crossover, and the GA control loop that determines which pairs of parents to cross are all in essence binomial experiments. We then show how to optimize each of these by utilizing a binomial distribution and sampling algorithms to dramatically speed the runtime of a GA relative to the common implementation. We implement our approach in the open-source Java library Chips-n-Salsa. Our experiments validate that the approach is orders of magnitude faster than the common GA implementation, yet produces solutions that are statistically equivalent in solution quality.

Paper Citation

in Harvard Style

A Cicirello V. (2024). Optimizing Genetic Algorithms Using the Binomial Distribution. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA; ISBN 978-989-758-721-4, SciTePress, pages 159-169. DOI: 10.5220/0013038300003837

in Bibtex Style

@conference{ecta24,
author={Vincent A Cicirello},
title={Optimizing Genetic Algorithms Using the Binomial Distribution},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA},
year={2024},
pages={159-169},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013038300003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA
TI - Optimizing Genetic Algorithms Using the Binomial Distribution
SN - 978-989-758-721-4
AU - A Cicirello V.
PY - 2024
SP - 159
EP - 169
DO - 10.5220/0013038300003837
PB - SciTePress