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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F13%3A00195551%21RIV14-MSM-21230___
rdf:type
n11:Vysledek skos:Concept
rdfs:seeAlso
http://rd.springer.com/chapter/10.1007/978-3-642-32922-7_7
dcterms:description
In 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. In 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.
dcterms:title
Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks
skos:prefLabel
Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks
skos:notation
RIV/68407700:21230/13:00195551!RIV14-MSM-21230___
n11:predkladatel
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n3:aktivita
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n3:aktivity
I
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n18:2014
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n15:7035586 n15:9121870
n3:druhVysledku
n9:D
n3:duvernostUdaju
n21:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
76391
n3:idVysledku
RIV/68407700:21230/13:00195551
n3:jazykVysledku
n8:eng
n3:klicovaSlova
GPAT; genetic programming; niching; hypercube-based encoding
n3:klicoveSlovo
n10:niching n10:GPAT n10:genetic%20programming n10:hypercube-based%20encoding
n3:kontrolniKodProRIV
[7212F33BFF2C]
n3:mistoKonaniAkce
Ostrava
n3:mistoVydani
Heidelberg
n3:nazevZdroje
Soft Computing Models in Industrial and Environmental Applications
n3:obor
n6:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n18:2013
n3:tvurceVysledku
Šnorek, Miroslav Drchal, Jan
n3:typAkce
n13:EUR
n3:wos
000312974600007
n3:zahajeniAkce
2012-09-05+02:00
s:issn
2194-5357
s:numberOfPages
10
n17:doi
10.1007/978-3-642-32922-7_7
n12:hasPublisher
Springer-Verlag
n19:isbn
978-3-642-32921-0
n22:organizacniJednotka
21230