An Exploration of Asocial and Social Learning in the Evolution of Variable-length Structures

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{ONeill:2021:CEC,

• author = "Michael O'Neill and Anthony Brabazon",
• booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
• title = "An Exploration of Asocial and Social Learning in the Evolution of Variable-length Structures",
• year = "2021",
• editor = "Yew-Soon Ong",
• pages = "2307--2314",
• address = "Krakow, Poland",
• month = "28 " # jun # "-1 " # jul,
• isbn13 = "978-1-7281-8393-0",
• abstract = "We wish to explore the contribution that asocial and social learning might play as a mechanism for self-adaptation in the search for variable-length structures by an evolutionary algorithm. An extremely challenging, yet simple to understand problem landscape is adopted where the probability of randomly finding a solution is approximately one in a trillion. A number of learning mechanisms operating on variable-length structures are implemented and their performance analysed. The social learning setup, which combines forms of both social and asocial learning in combination with evolution is found to be most performant, while the setups exclusively adopting evolution are incapable of finding solutions.",
• keywords = "genetic algorithms, genetic programming, Learning systems, Genomics, Evolutionary computation, Search problems, Approximation algorithms, Encoding",
• DOI = "doi:10.1109/CEC45853.2021.9504900",
• notes = "Also known as \cite{9504900}",

}

Genetic Programming entries for Michael O'Neill Anthony Brabazon

Citations