An experimental comparative study for interactive evolutionary computation problems

Authors:
Yago Sáez;Pedro Isasi;Javier Segovia;Asunción Mochón
Affiliations:
Universidad CARLOS III de Madrid, Leganés, Spain;Universidad CARLOS III de Madrid, Leganés, Spain;Facultad de Informática, Universidad Politécnica de Madrid, Boadilla del Monte, Spain;Departamento de Economía, Aplicada, Universidad Nacional de Educación a Distancia, Madrid, Spain
Venue:
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Year:
2006

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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.