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Parameter-less algorithm for evolutionary-based optimization

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  • Gregor Papa

Abstract

The development of a simple, adaptive, parameter-less search algorithm was initiated by the need for an algorithm that is able to find optimal solutions relatively quick, and without the need for a control-parameter-setting specialist. Its control parameters are calculated during the optimization process, according to the progress of the search. The algorithm is intended for continuous and combinatorial problems. The efficiency of the proposed parameter-less algorithm was evaluated using one theoretical and three real-world industrial optimization problems. A comparison with other evolutionary approaches shows that the presented adaptive parameter-less algorithm has a competitive convergence with regards to the comparable algorithms. Also, it proves algorithm’s ability to finding the optimal solutions without the need for predefined control parameters. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Gregor Papa, 2013. "Parameter-less algorithm for evolutionary-based optimization," Computational Optimization and Applications, Springer, vol. 56(1), pages 209-229, September.
    • Handle: RePEc:spr:coopap:v:56:y:2013:i:1:p:209-229
    DOI: 10.1007/s10589-013-9565-4
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    Cited by:

    1. Kostevšek, Anja & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Papa, Gregor & Petek, Janez, 2016. "The concept of an ecosystem model to support the transformation to sustainable energy systems," Applied Energy, Elsevier, vol. 184(C), pages 1460-1469.

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