Optimizing Models Using Continuous Ant Algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03145837" target="_blank" >RIV/68407700:21230/08:03145837 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Optimizing Models Using Continuous Ant Algorithms

Popis výsledku v původním jazyce

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 ableto optimize parameters of model units for this problem with the classification accuracy of 70%.

Název v anglickém jazyce

Optimizing Models Using Continuous Ant Algorithms

Popis výsledku anglicky

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 ableto optimize parameters of model units for this problem with the classification accuracy of 70%.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/KJB201210701" target="_blank" >KJB201210701: Automatická extrakce znalostí</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2008

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2nd International Conference on Inductive Modelling

ISBN

978-966-02-4889-2

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

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Název nakladatele

Ukr. INTEI

Místo vydání

Kiev

Místo konání akce

Kyjev

Datum konání akce

15. 9. 2008

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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