Self-regulated Population Size in Evolutionary Algorithms

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


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.


Population Size Evolutionary Algorithm Assortative Mating Mating Attempt Royal Road 
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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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