Using GP is NEAT: Evolving Compositional Pattern Production Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Assuncao:2018:EuroGP,

• author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro",
• title = "Using {GP} is {NEAT}: Evolving Compositional Pattern Production Functions",
• booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming",
• year = "2018",
• month = "4-6 " # apr,
• editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
• series = "LNCS",
• volume = "10781",
• publisher = "Springer Verlag",
• address = "Parma, Italy",
• pages = "3--18",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution",
• isbn13 = "978-3-319-77552-4",
• DOI = "doi:10.1007/978-3-319-77553-1_1",
• abstract = "The success of Artificial Neural Networks (ANNs) highly depends on their architecture and on how they are trained. However, making decisions regarding such domain specific issues is not an easy task, and is usually performed by hand, through an exhaustive trial-and-error process. Over the years, researches have developed and proposed methods to automatically train ANNs. One example is the HyperNEAT algorithm, which relies on NeuroEvolution of Augmenting Topologies (NEAT) to create Compositional Pattern Production Networks (CPPNs). CPPNs are networks that encode the mapping between neuron positions and the synaptic weight of the ANNs connection between those neurons. Although this approach has obtained some success, it requires meticulous parametrisation to work properly. In this article we present a comparison of different Evolutionary Computation methods to evolve Compositional Pattern Production Functions: structures that have the same goal as CPPNs, but that are encoded as functions instead of networks. In addition to NEAT three methods are used to evolve such functions: Genetic Programming (GP), Grammatical Evolution, and Dynamic Structured Grammatical Evolution. The results show that GP is able to obtain competitive performance, often surpassing the other methods, without requiring the fine tuning of the parameters.",
• notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018",

}

Genetic Programming entries for Filipe Assuncao Nuno Lourenco Penousal Machado Bernardete Ribeiro

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