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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F07%3A03130351%21RIV08-AV0-21230___
rdf:type
n5:Vysledek skos:Concept
dcterms:description
Evolutionary Algorithms (EAs) are well-known nature-inspired optimization methods. Diversity is an essenial aspect of each EA. It describes the variability of organisms in population. The lack of diversity is common problem - diversity should be preserved in order to evade local extremes (premature convergence). Niching algorithms are modifications of classical EAs. Niching is based on dividing the population into separate subpopulations - it spreads the organisms effectively all over the search space and hence making the overall population diverse. Using niching methods also requires setting of their parameters, which can be very difficult. This paper presents a novel way of diversity visualization based on physical system simulation. This visualization is helpful when designing and tuning niching algorithms but it has also other uses. The visualization will be presented on NEAT - the evolutionary algorithm which optimizes both the topology and the parameters of neural networks. Evolutionary Algorithms (EAs) are well-known nature-inspired optimization methods. Diversity is an essenial aspect of each EA. It describes the variability of organisms in population. The lack of diversity is common problem - diversity should be preserved in order to evade local extremes (premature convergence). Niching algorithms are modifications of classical EAs. Niching is based on dividing the population into separate subpopulations - it spreads the organisms effectively all over the search space and hence making the overall population diverse. Using niching methods also requires setting of their parameters, which can be very difficult. This paper presents a novel way of diversity visualization based on physical system simulation. This visualization is helpful when designing and tuning niching algorithms but it has also other uses. The visualization will be presented on NEAT - the evolutionary algorithm which optimizes both the topology and the parameters of neural networks. Evoluční algoritmy (EA) jsou známé přírodou inspirované optimalizační metody. U EA se často setkáváme s pojmem diverzity, která popisuje variabilitu organizmů v populaci. Nedostatečná diverzita je častým problémem a vede k předčasné konvergenci (uvíznutí v lokálním extrému). Niching algoritmy jsou modifikacemi klasických EA, které pomáhají problémy s nízkou diverzitou řešit. Ke své funkci však vyžadují správné nastavení parametrů, což nebývá vždy jednoduché. Tento článek představuje nový způsob vizualizace diverzity, založený na simulaci fyzikálního systému. Tato vizualizace je vhodná k nastavení niching algoritmů, ale má i obecnější využití. Praktické je demonstrována na NEAT - evolučním algoritmu optimalizujícím jak váhy, tak strukturu umělých neuronových sítí.
dcterms:title
Diversity visualization in evolutionary algorithms Vizualizace diverzity v evolučních algoritmech Diversity visualization in evolutionary algorithms
skos:prefLabel
Diversity visualization in evolutionary algorithms Vizualizace diverzity v evolučních algoritmech Diversity visualization in evolutionary algorithms
skos:notation
RIV/68407700:21230/07:03130351!RIV08-AV0-21230___
n3:strany
77;84
n3:aktivita
n8:Z n8:P
n3:aktivity
P(KJB201210701), Z(MSM6840770012)
n3:dodaniDat
n15:2008
n3:domaciTvurceVysledku
n7:9121870 n7:7035586
n3:druhVysledku
n17:D
n3:duvernostUdaju
n22:S
n3:entitaPredkladatele
n12:predkladatel
n3:idSjednocenehoVysledku
417699
n3:idVysledku
RIV/68407700:21230/07:03130351
n3:jazykVysledku
n16:eng
n3:klicovaSlova
diversity; evolutionary algorithms; niching; visualization
n3:klicoveSlovo
n11:visualization n11:diversity n11:evolutionary%20algorithms n11:niching
n3:kontrolniKodProRIV
[49A2317DD540]
n3:mistoKonaniAkce
Rožnov pod Radhoštěm
n3:mistoVydani
Ostrava
n3:nazevZdroje
Proceedings of 41th Spring International Conference MOSIS\'07
n3:obor
n21:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n18:KJB201210701
n3:rokUplatneniVysledku
n15:2007
n3:tvurceVysledku
Drchal, Jan Šnorek, Miroslav
n3:typAkce
n9:EUR
n3:zahajeniAkce
2007-04-24+02:00
n3:zamer
n14:MSM6840770012
s:numberOfPages
8
n20:hasPublisher
MARQ
n13:isbn
978-80-86840-30-7
n19:organizacniJednotka
21230