GEMO: Grammatical evolution memory optimization system

Meghana Kshirsagar, Rushikesh Jachak, Purva Chaudhari, Conor Ryan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationIJCCI 2020 - Proceedings of the 12th International Joint Conference on Computational Intelligence
EditorsJuan Julian Merelo, Jonathan Garibaldi, Christian Wagner, Thomas Back, Kurosh Madani, Kevin Warwick
PublisherSciTePress
Pages184-191
Number of pages8
ISBN (Electronic)9789897584756
ISBN (Print)9789897584756
DOIs
Publication statusPublished - 2020
Event12th International Joint Conference on Computational Intelligence, IJCCI 2020 - Virtual, Online
Duration: 2 Nov 20204 Nov 2020

Publication series

NameIJCCI 2020 - Proceedings of the 12th International Joint Conference on Computational Intelligence

Conference

Conference12th International Joint Conference on Computational Intelligence, IJCCI 2020
CityVirtual, Online
Period2/11/204/11/20

Keywords

  • Autoregressive time series forecasting
  • Evolutionary computation
  • Grammatical evolution
  • Memory optimization
  • Multi-objective optimization

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