Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks

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

@InProceedings{conf/softcomp/DrchalS12,

• author = "Jan Drchal and Miroslav Snorek",
• title = "Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks",
• booktitle = "7th International Conference, Soft Computing Models in Industrial and Environmental Applications SOCO-2012",
• year = "2013",
• editor = "Vaclav Snasel and Ajith Abraham and Emilio S. Corchado",
• volume = "188",
• series = "Advances in Intelligent Systems and Computing",
• pages = "63--72",
• address = "Ostrava, Czech Republic",
• month = sep # " 5th-7th",
• publisher = "Springer",
• bibdate = "2013-01-16",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/softcomp/soco2012.html#DrchalS12",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-32921-0",
• DOI = "doi:10.1007/978-3-642-32922-7_7",
• abstract = "n this paper we present a novel algorithm called GPAT (Genetic Programming of Augmenting Topologies) which evolves Genetic Programming (GP) trees in a similar way as a well-established neuro-evolutionary algorithm NEAT (NeuroEvolution of Augmenting Topologies) does. The evolution starts from a minimal form and gradually adds structure as needed. A niching evolutionary algorithm is used to protect individuals of a variable complexity in a single population. Although GPAT is a general approach we employ it mainly to evolve artificial neural networks by means of Hypercube-based indirect encoding which is an approach allowing for evolution of large-scale neural networks having theoretically unlimited size. We perform also experiments for directly encoded problems. The results show that GPAT outperforms both GP and NEAT taking the best of both.",

}

Genetic Programming entries for Jan Drchal Miroslav Snorek

Citations