Genetic Programming and Evolvable Machines

, Volume 11, Issue 2, pp 227–246 | Cite as

EvAg: a scalable peer-to-peer evolutionary algorithm

  • J. L. J. Laredo
  • A. E. Eiben
  • M. van Steen
  • J. J. Merelo
Original Paper


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.


Peer-to-peer computing Evolutionary algorithms Scalability analysis Diversity 



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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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • J. L. J. Laredo
    • 1
  • A. E. Eiben
    • 2
  • M. van Steen
    • 2
  • J. J. Merelo
    • 1
  1. 1.University of Granada, ATC-ETSITGranadaSpain
  2. 2.Department of Computer ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands

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