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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F08%3A03145837%21RIV09-MSM-21230___
rdf:type
n5:Vysledek skos:Concept
dcterms:description
While constructing inductive models of a given system, we need to optimize parameters of units the system is composed of. These parameters are often real-valued variables and we can use a large scale of continuous optimization methods to locate their optimum. Each of these methods can give different results for problems of various nature or complexity. In our experiments, the usually best performing gradient based Quasi-Newton method was unable to optimize parameters for a well known problem of two intertwined spirals; its classification accuracy was close to 50%. Therefore, we compared several continuous optimization algorithms performance on this particular problem. Our results show that two probabilistic algorithms inspired by ant behaviour are able to optimize parameters of model units for this problem with the classification accuracy of 70%. While constructing inductive models of a given system, we need to optimize parameters of units the system is composed of. These parameters are often real-valued variables and we can use a large scale of continuous optimization methods to locate their optimum. Each of these methods can give different results for problems of various nature or complexity. In our experiments, the usually best performing gradient based Quasi-Newton method was unable to optimize parameters for a well known problem of two intertwined spirals; its classification accuracy was close to 50%. Therefore, we compared several continuous optimization algorithms performance on this particular problem. Our results show that two probabilistic algorithms inspired by ant behaviour are able to optimize parameters of model units for this problem with the classification accuracy of 70%. While constructing inductive models of a given system, we need to optimize parameters of units the system is composed of. These parameters are often real-valued variables and we can use a large scale of continuous optimization methods to locate their optimum. Each of these methods can give different results for problems of various nature or complexity. In our experiments, the usually best performing gradient based Quasi-Newton method was unable to optimize parameters for a well known problem of two intertwined spirals; its classification accuracy was close to 50%. Therefore, we compared several continuous optimization algorithms performance on this particular problem. Our results show that two probabilistic algorithms inspired by ant behaviour are able to optimize parameters of model units for this problem with the classification accuracy of 70%.
dcterms:title
Optimizing Models Using Continuous Ant Algorithms Optimizing Models Using Continuous Ant Algorithms Optimizing Models Using Continuous Ant Algorithms
skos:prefLabel
Optimizing Models Using Continuous Ant Algorithms Optimizing Models Using Continuous Ant Algorithms Optimizing Models Using Continuous Ant Algorithms
skos:notation
RIV/68407700:21230/08:03145837!RIV09-MSM-21230___
n3:aktivita
n4:Z n4:P
n3:aktivity
P(KJB201210701), Z(MSM6840770012)
n3:dodaniDat
n11:2009
n3:domaciTvurceVysledku
n13:9608362 n13:1266500
n3:druhVysledku
n17:D
n3:duvernostUdaju
n22:S
n3:entitaPredkladatele
n21:predkladatel
n3:idSjednocenehoVysledku
385273
n3:idVysledku
RIV/68407700:21230/08:03145837
n3:jazykVysledku
n15:eng
n3:klicovaSlova
Ant Algorithms; Continuous Optimization; Inductive Modelling
n3:klicoveSlovo
n14:Ant%20Algorithms n14:Continuous%20Optimization n14:Inductive%20Modelling
n3:kontrolniKodProRIV
[B5E70E22104F]
n3:mistoKonaniAkce
Kyjev
n3:mistoVydani
Kiev
n3:nazevZdroje
Proceedings of the 2nd International Conference on Inductive Modelling
n3:obor
n12:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n20:KJB201210701
n3:rokUplatneniVysledku
n11:2008
n3:tvurceVysledku
Kovářík, Oleg Kordík, Pavel
n3:typAkce
n18:WRD
n3:zahajeniAkce
2008-09-15+02:00
n3:zamer
n16:MSM6840770012
s:numberOfPages
5
n10:hasPublisher
Ukr. INTEI
n19:isbn
978-966-02-4889-2
n9:organizacniJednotka
21230