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Evolutionary Algorithms: Perspectives on the Evolution of Parallel Models

  • F. Fernández de VegaEmail author
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 616)

Abstract

This chapter discusses the inherent parallel nature of evolutionary algorithms, and the role this parallelism can take when implementing them on different hardware architectures. We show the interest in studying ephemeral behaviors that distributed computing resources may feature and some EA’s self-properties of interest, such as the fault-tolerant nature that helps to fight the churn phenomenon. Moreover, interactive versions of EAs, which require distributed computing systems, allow to incorporate human based knowledge within the algorithm at different levels, providing new means for improving their computing capabilities while also requiring a proper analysis of human behavior under an EA framework. A proper understanding of ephemeral properties of hardware resources, human behavior in interactive applications and intrinsic parallel behaviors of population based algorithms will lead to significant improvements.

Keywords

Evolutionary Algorithm Genetic Programming Fitness Evaluation Desktop Grid Volunteer Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work is supported by EU Merie Curie actions, FP7-PEOPLE-2013-IRSES, Grant 612689 ACoBSEC; MINECO project EphemeCH (TIN2014-56494-C4-P) and Gobierno de Extremadura,Consejería de Economía-Comercio e Innovación y FEDER, proyect GRU10029.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Centro Universitario de MéridaUniversidad de ExtremaduraMérida (Badajoz)Spain

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