GELAB and Hybrid Optimization Using Grammatical Evolution

Muhammad Adil Raja, Aidan Murphy, Conor Ryan

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

Abstract

Grammatical Evolution (GE) is a well known technique for program synthesis and evolution. Much has been written in the past about its research and applications. This paper presents a novel approach to performing hybrid optimization using GE. GE is used for structural search in the program space while other meta-heuristic algorithms are used for numerical optimization of the searched programs. The hybridised GE system was implemented in GELAB, a Matlab toolbox for GE.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings
EditorsCesar Analide, Paulo Novais, David Camacho, Hujun Yin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages292-303
Number of pages12
ISBN (Print)9783030623616
DOIs
Publication statusPublished - 2020
Event21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 - Guimaraes, Portugal
Duration: 4 Nov 20206 Nov 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12489 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020
Country/TerritoryPortugal
CityGuimaraes
Period4/11/206/11/20

Keywords

  • GELAB
  • Genetic algorithms
  • Grammatical evolution
  • Hybrid optimization
  • Simulated annealing
  • Swarm optimization

Fingerprint

Dive into the research topics of 'GELAB and Hybrid Optimization Using Grammatical Evolution'. Together they form a unique fingerprint.

Cite this