@inproceedings{cb0aa3569f5e4ef0a989823b3ad8a23f,
title = "GELAB and Hybrid Optimization Using Grammatical Evolution",
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.",
keywords = "GELAB, Genetic algorithms, Grammatical evolution, Hybrid optimization, Simulated annealing, Swarm optimization",
author = "Raja, {Muhammad Adil} and Aidan Murphy and Conor Ryan",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 ; Conference date: 04-11-2020 Through 06-11-2020",
year = "2020",
doi = "10.1007/978-3-030-62362-3_26",
language = "English",
isbn = "9783030623616",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "292--303",
editor = "Cesar Analide and Paulo Novais and David Camacho and Hujun Yin",
booktitle = "Intelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings",
}