Parameter Less Local Optimizer with Linkage Identification for Deterministic Order k Decomposable Problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00184061" target="_blank" >RIV/68407700:21230/11:00184061 - isvavai.cz</a>

Výsledek na webu

<a href="http://dl.acm.org/citation.cfm?id=2001656" target="_blank" >http://dl.acm.org/citation.cfm?id=2001656</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/2001576.2001656" target="_blank" >10.1145/2001576.2001656</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Parameter Less Local Optimizer with Linkage Identification for Deterministic Order k Decomposable Problems

Popis výsledku v původním jazyce

A simple parameter-less local optimizer able to solve deterministic problems with building blocks of bounded order is proposed in this article. The algorithm is able to learn and use linkage information during the run. The algorithm is algorithmically simple, easy to implement and with the exception of termination condition, it is completely parameter-free - there is thus no need to tune the population size and other parameters to the problem at hand. An empirical comparison on 3 decomposable functions,each with uniformly scaled building blocks of size 5 and 8, was carried out. The algorithm exhibits quadratic scaling with the problem dimensionality, but the comparison with the extended compact genetic algorithm and Bayesian optimization algorithm shows that it needs lower or comparable number of fitness function evaluations on the majority of functions for the tested problem dimensionalities.

Název v anglickém jazyce

Parameter Less Local Optimizer with Linkage Identification for Deterministic Order k Decomposable Problems

Popis výsledku anglicky

A simple parameter-less local optimizer able to solve deterministic problems with building blocks of bounded order is proposed in this article. The algorithm is able to learn and use linkage information during the run. The algorithm is algorithmically simple, easy to implement and with the exception of termination condition, it is completely parameter-free - there is thus no need to tune the population size and other parameters to the problem at hand. An empirical comparison on 3 decomposable functions,each with uniformly scaled building blocks of size 5 and 8, was carried out. The algorithm exhibits quadratic scaling with the problem dimensionality, but the comparison with the extended compact genetic algorithm and Bayesian optimization algorithm shows that it needs lower or comparable number of fitness function evaluations on the majority of functions for the tested problem dimensionalities.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

ISBN

978-1-4503-0557-0

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

577-584

Název nakladatele

ACM

Místo vydání

New York

Místo konání akce

Dublin

Datum konání akce

12. 7. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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