GELAB and Hybrid Optimization Using Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/ideal/RajaMR20,

• author = "Muhammad Adil Raja and Aidan Murphy and Conor Ryan",
• title = "{GELAB} and Hybrid Optimization Using Grammatical Evolution",
• booktitle = "Intelligent Data Engineering and Automated Learning, IDEAL 2020, Part I",
• year = "2020",
• editor = "Cesar Analide and Paulo Novais and David Camacho and Hujun Yin",
• volume = "12489",
• series = "Lecture Notes in Computer Science",
• pages = "292--303",
• address = "Guimaraes, Portugal",
• month = nov # " 4-6",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, simulated annealing, swarm optimisation, PSO, hybrid optimisation, GELAB",
• isbn13 = "978-3-030-62361-6",
• bibdate = "2020-11-14",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ideal/ideal2020-1.html#RajaMR20",
• DOI = "doi:10.1007/978-3-030-62362-3_26",
• 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 optimisation using GE. GE is used for structural search in the program space while other meta-heuristic algorithms are used for numerical optimisation of the searched programs. The hybridised GE system was implemented in GELAB, a Matlab toolbox for GE.",
• notes = "Bio-computing and Developmental Systems (BDS) Research Group, University of Limerick, Limerick, Ireland",

}

Genetic Programming entries for Adil Raja Aidan Murphy Conor Ryan

Citations