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  • Evolutionary algorithms applied in real domain should profit from information about the local fitness function curvature. This paper presents an initial study of an evolutionary strategy with a novel approach for learning the covariance matrix of a Gaussian distribution. The learning method is based on estimation of the fitness landscape contour line between the selected and discarded individuals. The distribution learned this way is then used to generate new population members. The algorithm presented here is the first attempt to construct the Gaussian distribution this way and should be considered only a proof of concept; nevertheless, the empirical comparison on low-dimensional quadratic functions shows that our approach is viable and with respect to the number of evaluations needed to find a solution of certain quality, it is comparable to the state-of-the-art CMA-ES in case of sphere function and outperforms the CMA-ES in case of elliptical function.
  • Evolutionary algorithms applied in real domain should profit from information about the local fitness function curvature. This paper presents an initial study of an evolutionary strategy with a novel approach for learning the covariance matrix of a Gaussian distribution. The learning method is based on estimation of the fitness landscape contour line between the selected and discarded individuals. The distribution learned this way is then used to generate new population members. The algorithm presented here is the first attempt to construct the Gaussian distribution this way and should be considered only a proof of concept; nevertheless, the empirical comparison on low-dimensional quadratic functions shows that our approach is viable and with respect to the number of evaluations needed to find a solution of certain quality, it is comparable to the state-of-the-art CMA-ES in case of sphere function and outperforms the CMA-ES in case of elliptical function. (en)
  • Evoluční algoritmy v oblasti reálných čísel by měly profitovat z informací o lokálním zakřivení účelové funkce. Tento článek je úvodní studií evoluční strategie s inovativním přístupem k učení kovarianční matice Gaussova rozdělení. Metoda učení je založena na odhadu vrstevnice účelové funkce ležící mezi dobrými a špatnými jedinci. Takto naučené rozdělení je pak použito ke generování nových členů populace. Algoritmus, který je zde prezentován, je prvním pokusem o konstrukci Gaussova rozdělení tímto způsobem; přesto empirická porovnání na nízkorozměrných kvadratických funkcích ukazují, že s ohledem na počet ohodnocení účelové funkce potřebný k dosažení řešení jisté kvality je náš přístup porovnatelný s algoritmem CMA-ES, který je považován za špičkuv této oblasti. (cs)
Title
  • Estimation of Fitness Landscape Contours in EAs
  • Odhadování vrstevnic účelové funkce v evolučních algoritmech (cs)
  • Estimation of Fitness Landscape Contours in EAs (en)
skos:prefLabel
  • Estimation of Fitness Landscape Contours in EAs
  • Odhadování vrstevnic účelové funkce v evolučních algoritmech (cs)
  • Estimation of Fitness Landscape Contours in EAs (en)
skos:notation
  • RIV/68407700:21230/07:03131105!RIV08-MSM-21230___
http://linked.open.../vavai/riv/strany
  • 562;569
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6840770012)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 420477
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/07:03131105
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • estimation of distribution algorithms; evolutionary computation; function optimization; learnable evolution model; separating ellipsoid (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [05C4AA3509AF]
http://linked.open...v/mistoKonaniAkce
  • London
http://linked.open...i/riv/mistoVydani
  • New York
http://linked.open...i/riv/nazevZdroje
  • Proceedings of Genetic and Evolutionary Computation Conference 2007
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Pošík, Petr
  • Franc, V.
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • ACM
https://schema.org/isbn
  • 978-1-59593-698-1
http://localhost/t...ganizacniJednotka
  • 21230
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