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Self-regulated Population Size in Evolutionary Algorithms

  • Carlos Fernandes
  • Agostinho Rosa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)

Abstract

In this paper we analyze a new method for an adaptive variation of Evolutionary Algorithms (EAs) population size: the Self-Regulated Population size EA (SRP-EA). An empirical evaluation of the method is provided by comparing the new proposal with the CHC algorithm and other well known EAs with varying population. A fitness landscape generator was chosen to test and compare the algorithms: the Spear’s multimodal function generator. The performance of the algorithms was measured in terms of success rate, quality of the solutions and evaluations needed to attain them over a wide range of problem instances. We will show that SRP-EA performs well on these tests and appears to overcome some recurrent drawbacks of traditional EAs which lead them to local optima premature convergence. Also, unlike other methods, SRP-EA seems to self-regulate its population size according to the state of the search.

Keywords

Population Size Evolutionary Algorithm Assortative Mating Mating Attempt Royal Road 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Carlos Fernandes
    • 1
  • Agostinho Rosa
    • 1
  1. 1.LaSEEB-ISR-ISTTechnical Univ. of Lisbon (IST) 

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