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An Experimental Comparative Study for Interactive Evolutionary Computation Problems

  • Yago Sáez
  • Pedro Isasi
  • Javier Segovia
  • Asunción Mochón
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)

Abstract

This paper presents an objective experimental comparative study between four algorithms: the Genetic Algorithm, the Fitness Prediction Genetic Algorithm, the Population Based Incremental Learning algorithm and the purposed method based on the Chromosome Appearance Probability Matrix. The comparative is done with a non subjective evaluation function. The main objective is to validate the efficiency of several methods in Interactive Evolutionary Computation environments. The most important constraint of working within those environments is the user interaction, which affects the results adding time restrictions for the experimentation stage and subjectivity to the validation. The experiments done in this paper replace user interaction with several approaches avoiding user limitations. So far, the results show the efficiency of the purposed algorithm in terms of quality of solutions and convergence speed, two known keys to decrease the user fatigue.

Keywords

Genetic Algorithm Mutation Operator Incremental Learn Valid Solution Experimental Comparative Study 
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

  • Yago Sáez
    • 1
  • Pedro Isasi
    • 1
  • Javier Segovia
    • 2
  • Asunción Mochón
    • 3
  1. 1.Universidad CARLOS III de MadridLeganésSpain
  2. 2.Facultad de InformáticaUniversidad Politécnica de MadridBoadilla del MonteSpain
  3. 3.Departamento de Economía, AplicadaUniversidad Nacional de Educación a DistanciaMadridSpain

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