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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F14%3APU107975%21RIV15-MSM-26220___
rdf:type
skos:Concept n10:Vysledek
dcterms:description
Automatic optimization techniques, such as evolutionary algorithms, have become popular in the recent years as a general, simple, robust, and scalable solution which can be applied when other optimization method fails. Recently, many evolutionary and/or genetic based optimization frameworks and libraries have been developed and lot of them is freely available. On the other hand, there are not many tools in optimization field that allows the researchers to implement own code, modify existing code or compare different algorithms. This paper proposes a new grammar driven genetic programming based framework implemented in cross-platform Java programming language which allows to implement own code, modify existing, and analyze algorithms. The framework described in this paper addresses the problem of flexibility, modularity, multiplatformness, and presents a general architecture for evolutionary optimization based on genetic programming driven by context free grammar distributed under the LGPL license suit Automatic optimization techniques, such as evolutionary algorithms, have become popular in the recent years as a general, simple, robust, and scalable solution which can be applied when other optimization method fails. Recently, many evolutionary and/or genetic based optimization frameworks and libraries have been developed and lot of them is freely available. On the other hand, there are not many tools in optimization field that allows the researchers to implement own code, modify existing code or compare different algorithms. This paper proposes a new grammar driven genetic programming based framework implemented in cross-platform Java programming language which allows to implement own code, modify existing, and analyze algorithms. The framework described in this paper addresses the problem of flexibility, modularity, multiplatformness, and presents a general architecture for evolutionary optimization based on genetic programming driven by context free grammar distributed under the LGPL license suit
dcterms:title
Java Evolutionary Framework based on Genetic Programming Java Evolutionary Framework based on Genetic Programming
skos:prefLabel
Java Evolutionary Framework based on Genetic Programming Java Evolutionary Framework based on Genetic Programming
skos:notation
RIV/00216305:26220/14:PU107975!RIV15-MSM-26220___
n3:aktivita
n7:S
n3:aktivity
S
n3:dodaniDat
n20:2015
n3:domaciTvurceVysledku
n16:2140276 n16:2629291
n3:druhVysledku
n4:D
n3:duvernostUdaju
n14:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
23303
n3:idVysledku
RIV/00216305:26220/14:PU107975
n3:jazykVysledku
n18:eng
n3:klicovaSlova
Framework, Genetic Programming, Optimization
n3:klicoveSlovo
n12:Genetic%20Programming n12:Optimization n12:Framework
n3:kontrolniKodProRIV
[5ACDF6AA2B35]
n3:mistoKonaniAkce
New Delhi, Noida
n3:mistoVydani
New Delhi, Republic of India
n3:nazevZdroje
2014 International Conference on Signal Processing and Integrated Networks (SPIN)
n3:obor
n17:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
4
n3:rokUplatneniVysledku
n20:2014
n3:tvurceVysledku
Singh, Anushikha Burget, Radim Karásek, Jan Dutta, Malay Kishore
n3:typAkce
n6:WRD
n3:zahajeniAkce
2014-02-20+01:00
s:numberOfPages
7
n19:hasPublisher
Neuveden
n8:isbn
978-1-4799-2865-1
n11:organizacniJednotka
26220