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| Description
| - The vast majority of population based optimization algorithms use selection in such a way that the non selected individuals do not have any effect on the evolution at all, even though they may carry a valueable information information about the search space areas where the search should be suppressed and/or about the local shape of the search distribution. This article describes a unified way of taking advantage of the information hidden in the non selected individuals in the framework of evolutionary algorithms: first, build a classifier discriminating between selected and non selected individuals, then turn the description of selected individuals into a search distribution, and sample new offspring from it. The concept is verified by a simple real valued evolutionary algorithm which outperforms the state of the art evolutionary strategy with covariance matrix adaptation (CMA ES) on selected test functions in all tested search space dimensionalities.
- The vast majority of population based optimization algorithms use selection in such a way that the non selected individuals do not have any effect on the evolution at all, even though they may carry a valueable information information about the search space areas where the search should be suppressed and/or about the local shape of the search distribution. This article describes a unified way of taking advantage of the information hidden in the non selected individuals in the framework of evolutionary algorithms: first, build a classifier discriminating between selected and non selected individuals, then turn the description of selected individuals into a search distribution, and sample new offspring from it. The concept is verified by a simple real valued evolutionary algorithm which outperforms the state of the art evolutionary strategy with covariance matrix adaptation (CMA ES) on selected test functions in all tested search space dimensionalities. (en)
- Většina populačních optimalizačních algoritmů používá selekci takovým způsobem, že jedinci, kteří nejsou vybraní, nemají na další evoluci žádný vliv, ačkoli mohou nést důležitou informaci - informaci o těch oblastech prohledávaného prostoru, kde by mělo být hledání potlačeno, a/nebo o lokálním tvaru vyhledávacího rozdělení. Tento článek popisuje způsob, jak využít informaci skrytou v nevybraných jedincích v rámci evolučních algoritmů: nejprve je vytvořen klasifikátor oddělující vybrané a nevybrané jedince, popis vybraných jedinců je převeden na pravděpodobnostní model a noví jedinci jsou navzorkováni z tohoto modelu. Tento koncept je ověřen na jednoduchém evolučním algoritmu v reálné oblasti, který na zvolených testovacích funkcích překonává evoluční algoritmus s adaptací kovarianční matice. (cs)
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| Title
| - Optimization via Classification
- Optimalizace použitím klasifikace (cs)
- Optimization via Classification (en)
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| skos:prefLabel
| - Optimization via Classification
- Optimalizace použitím klasifikace (cs)
- Optimization via Classification (en)
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| skos:notation
| - RIV/68407700:21230/07:03131926!RIV08-MSM-21230___
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| http://linked.open.../vavai/riv/strany
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/07:03131926
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - Gauss distribution; classifier; elliptic; optimization (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...v/mistoKonaniAkce
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| http://linked.open...i/riv/mistoVydani
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| http://linked.open...i/riv/nazevZdroje
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
| |
| http://linked.open...UplatneniVysledku
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| http://linked.open...iv/tvurceVysledku
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| http://linked.open...vavai/riv/typAkce
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| http://linked.open.../riv/zahajeniAkce
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| http://linked.open...n/vavai/riv/zamer
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| number of pages
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| http://purl.org/ne...btex#hasPublisher
| - Vysoké učení technické v Brně
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| https://schema.org/isbn
| |
| http://localhost/t...ganizacniJednotka
| |
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