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EvAg: a scalable peer-to-peer evolutionary algorithm

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Abstract

This paper studies the scalability of an Evolutionary Algorithm (EA) whose population is structured by means of a gossiping protocol and where the evolutionary operators act exclusively within the local neighborhoods. This makes the algorithm inherently suited for parallel execution in a peer-to-peer fashion which, in turn, offers great advantages when dealing with computationally expensive problems because distributed execution implies massive scalability. In this paper we show another advantage of this algorithm: We experimentally demonstrate that it scales up better than traditional alternatives even when executed in a sequential fashion. In particular, we analyze the behavior of several EAs on well-known deceptive trap functions with varying sizes and levels of deceptiveness. The results show that the new EA requires smaller optimal population sizes and fewer fitness evaluations to reach solutions. The relative advantage of the new EA is more outstanding as problem hardness and size increase. In some cases the new algorithm reduces the computational efforts of the traditional EAs by several orders of magnitude.

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Notes

  1. Originally, Ackley’s trap functions use \(z={\frac{3l} {4}}\) , however, [7] demonstrates that trap functions are fully easy under such settings.

  2. All the source code for the experiments is available from our Subversion repository at http://www.forja.rediris.es/svn/geneura/evogen published under GPL v3. Accessed on September 2009.

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Acknowledgments

This work has been supported by the Spanish MICYT project TIN2007-68083-C02-01, the Junta de Andalucia CICE project P06-TIC-02025 and the Granada University PIUGR 9/11/06 project.

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Authors and Affiliations

  1. University of Granada, ATC-ETSIT, C. Periodista Daniel Saucedo Aranda, 18071, Granada, Spain

    J. L. J. Laredo & J. J. Merelo

  2. Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    A. E. Eiben & M. van Steen

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  1. J. L. J. Laredo
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  2. A. E. Eiben
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  3. M. van Steen
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  4. J. J. Merelo
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Correspondence to J. L. J. Laredo.

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Laredo, J.L.J., Eiben, A.E., van Steen, M. et al. EvAg: a scalable peer-to-peer evolutionary algorithm. Genet Program Evolvable Mach 11, 227–246 (2010). https://doi.org/10.1007/s10710-009-9096-z

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  • DOI: https://doi.org/10.1007/s10710-009-9096-z

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