Optimizing Models Using Continuous Ant Algorithms

Result code in 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>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Optimizing Models Using Continuous Ant Algorithms

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

Czech name

Optimizing Models Using Continuous Ant Algorithms

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

Type

D - Article in proceedings

CEP classification

IN - Informatics

OECD FORD branch

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Project

<a href="/en/project/KJB201210701" target="_blank" >KJB201210701: Automated Knowledge Extraction</a><br>

Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2008

Confidentiality

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

Article name in the collection

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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Number of pages

5

Pages from-to

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Publisher name

Ukr. INTEI

Place of publication

Kiev

Event location

Kyjev

Event date

Sep 15, 2008

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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