A Hybrid Fuzzy-Genetic Algorithm

  • Agustin Leon-Barranco
  • Carlos A. Reyes-Garcia
  • Ramon Zatarain-Cabada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


In this paper we present a hybrid fuzzy-genetic algorithm for the feature and instance subset selection problem. This algorithm combines a hybrid meta-heuristic algorithm and a fuzzy self-adaptive genetic algorithm with a rotary circular crossover which is based on a half uniform crossover. The best individual in the initial population is used as initial solution of the hybrid meta-heuristic algorithm with the purpose of improving its fitness; this method is a combination of simulated annealing, taboo search and hill-climbers and allows us to speed up the convergence of the initial population. When running, the genetic algorithm adjusts its own control parameters, and the adaptability of control parameters is directed by means of two fuzzy inference systems. Besides the description of the novel evolutionary algorithm, we present the results obtained during the experiments on several known databases and on an infant cry corpus.


Genetic Algorithm Fuzzy Inference System Current Solution Crossover Operator Automatic Speech Recognition 
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

  • Agustin Leon-Barranco
    • 1
  • Carlos A. Reyes-Garcia
    • 1
  • Ramon Zatarain-Cabada
    • 2
  1. 1.Instituto Nacional de AstrofísicaÓptica y Electrónica (INAOE)Sta. Ma. TonanzintlaMéxico
  2. 2.Instituto Tecnológico de CuliacánCuliacánMéxico

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