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Memetic Computing

Erschienen in:

01.12.2013 | Regular research paper

Evaluating the Multiple Offspring Sampling framework on complex continuous optimization functions

verfasst von: Antonio LaTorre, Santiago Muelas, José-María Peña

Erschienen in: Memetic Computing | Ausgabe 4/2013

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Abstract

In this contribution we present a study on the combination of Differential Evolution and the IPOP-CMA-ES algorithms. The hybrid algorithm has been constructed by using the Multiple Offspring Sampling framework, which allows the seamless combination of multiple metaheuristics in a dynamic algorithm capable of adjusting the participation of each of the composing algorithms according to their current performance. In this study we analyze the existing synergies, if any, emerging from the combination of the two algorithms. For this purpose, the COCO suite used in BBOB 2009 and 2010 Workshops has been used. The experimental results on the noiseless testbed show a robust behavior of the algorithm and a good scalability as the dimensionality increases. In the noisy testbed, the algorithm shows a good performance on functions with moderate to severe noise.

Vorheriger Artikel Distributed mixed variant differential evolution algorithms for unconstrained global optimization

Nächster Artikel Design and simulation of FIR band pass and band stop filters using gravitational search algorithm

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The complete 120 results in the noiseless testbed can be accessed in the following URL: http://laurel.datsi.fi.upm.es/research.

The complete 150 results in the noisy testbed can be accessed in the following URL: http://laurel.datsi.fi.upm.es/research.

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Titel: Evaluating the Multiple Offspring Sampling framework on complex continuous optimization functions
verfasst von: Antonio LaTorre
Santiago Muelas
José-María Peña
Publikationsdatum: 01.12.2013
Verlag: Springer Berlin Heidelberg
Erschienen in: Memetic Computing / Ausgabe 4/2013
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI: https://doi.org/10.1007/s12293-013-0120-8

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