GEMO: Grammatical Evolution Memory Optimization System

Meghana Kshirsagar, Rushikesh Jachak, Purva Chaudhari, Conor Ryan

2020

Abstract

In Grammatical Evolution (GE) individuals occupy more space than required, that is, the Actual Length of the individuals is longer than their Effective Length. This has major implications for scaling GE to complex problems that demand larger populations and complex individuals. We show how these two lengths vary for different sizes of population, demonstrating that Effective Length is relatively independent of population size, but that the Actual Length is proportional to it. We introduce Grammatical Evolution Memory Optimization (GEMO), a two-stage evolutionary system that uses a multi-objective approach to identify the optimal, or at least, near-optimal, genome length for the problem being examined. It uses a single run with a multi-objective fitness function defined to minimize the error for the problem being tackled along with maximizing the ratio of Effective to Actual Genome Length leading to better utilization of memory and hence, computational speedup. Then, in Stage 2, standard GE runs are performed restricting the genome length to the length obtained in Stage 1. We demonstrate this technique on different problem domains and show that in all cases, GEMO produces individuals with the same fitness as standard GE but significantly improves memory usage and reduces computation time.

Paper Citation

in Harvard Style

Kshirsagar M., Jachak R., Chaudhari P. and Ryan C. (2020). GEMO: Grammatical Evolution Memory Optimization System. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA; ISBN 978-989-758-475-6, SciTePress, pages 184-191. DOI: 10.5220/0010106501840191

in Bibtex Style

@conference{ecta20,
author={Meghana Kshirsagar and Rushikesh Jachak and Purva Chaudhari and Conor Ryan},
title={GEMO: Grammatical Evolution Memory Optimization System},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA},
year={2020},
pages={184-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010106501840191},
isbn={978-989-758-475-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA
TI - GEMO: Grammatical Evolution Memory Optimization System
SN - 978-989-758-475-6
AU - Kshirsagar M.
AU - Jachak R.
AU - Chaudhari P.
AU - Ryan C.
PY - 2020
SP - 184
EP - 191
DO - 10.5220/0010106501840191
PB - SciTePress