Beitrag in einem Tagungsband
A Framework for Multi-Model EDAs with Model Recombination

Details zur Publikation
Niemczyk, S.; Weise, T.
David Hutchison, Takeo Kanade, Josef Kittler, Jon M.
Springer-Verlag GmbH: Berlin, Germany
Proceedings of the 4th European Event on Bio-Inspired Algorithms for Continuous Parameter Optimisation (EvoNUM’11), Applications of Evolutionary Computation ‐ Proceedings of EvoApplications 2011: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, Part 1 (EvoAPPLICATIONS’11)
Titel der Buchreihe:
Theoretical Computer Science and General Issues (SL 1), Lecture Notes in Computer Science (LNCS)

Zusammenfassung, Abstract
Estimation of Distribution Algorithms (EDAs) are evolutionary optimization methods that build models which estimate the distribution of promising regions in the search space. Conventional EDAs use only one single model at a time. One way to efficiently explore multiple areas of the search space is to use multiple models in parallel. In this paper, we present a general framework for both single- and multi-model EDAs.We propose the use of clustering to divide selected individuals into different groups, which are then utilized to build separate models. For the multi-model case, we introduce the concept of model recombination. This novel framework has great generality, encompassing the traditional Evolutionary Algorithm and the EDA as its extreme cases. We instantiate our framework in the form of a real-valued algorithm and apply this algorithm to some well-known benchmark functions. Numerical results show that both single- and multi-model EDAs have their own strengths and weaknesses, and that the multi-model EDA is able to prevent premature convergence.


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Zuletzt aktualisiert 2019-25-07 um 12:42