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A Framework for Agent-Based Evaluation of Genetic Algorithms

  • David F. Barrero
  • David Camacho
  • María D. R-Moreno
Part of the Studies in Computational Intelligence book series (SCI, volume 237)

Abstract

Genetic Algorithms (GA) are a set of algorithms that use biological evolution as inspiration to solve search problems. One of the difficulties found when working with GA are the several parameters that have to be set and the many details that can be tunned in the GA. Usually it leads to the execution of several experiments in order to study how the GA behaves under different circumstances. In general it requires several computational resources and time to code the same algorithm with slight differences several times. In this paper we propose a framework based on agent technology able to parallelize the experiment and to split it into several components. It is complemented with a description of how this framework can be used in the evolution of regular expressions.

Keywords

Genetic Algorithm Crossover Operator Regular Expression Chromosome Length Grammatical Evolution 
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 2009

Authors and Affiliations

  • David F. Barrero
    • 1
  • David Camacho
    • 2
  • María D. R-Moreno
    • 1
  1. 1.Computer Engineering DepartmentUniversidad de AlcaláMadridSpain
  2. 2.Computer Science DepartmentUniversidad Autónoma de MadridMadridSpain

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