Adaptively Evolving Probabilities of Genetic Operators

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Vafaee:2008:ICMLA,
  title =        "Adaptively Evolving Probabilities of Genetic
                 Operators",
  author =       "Fatemeh Vafaee and Weimin Xiao and Peter C. Nelson and 
                 Chi Zhou",
  booktitle =    "Seventh International Conference on Machine Learning
                 and Applications, ICMLA '08",
  year =         "2008",
  month =        "11-13 " # dec,
  pages =        "292--299",
  address =      "La Jolla, San Diego, USA",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, mathematical operators,
                 probability adaptive method, differential evolution,
                 evolved evolutionary algorithm, genetic operator
                 probability, numerical optimization model,
                 supplementary mutation operator",
  DOI =          "doi:10.1109/ICMLA.2008.45",
  abstract =     "This work is concerned with proposing an adaptive
                 method to dynamically adjust genetic operator
                 probabilities throughout the evolutionary process. The
                 proposed method relies on the individual preferences of
                 each chromosome, rather than the global behavior of the
                 whole population. Hence, each individual carries its
                 own set of parameters, including the probabilities of
                 the genetic operators. The carried parameters undergo
                 the same evolutionary process as the carriers--the
                 chromosomes - do. We call this method Evolved
                 Evolutionary Algorithm (E2A) as it has an additional
                 evolutionary process to evolve control parameters.
                 Furthermore, E2A employs a supplementary mutation
                 operator (DE-mutation) which uses the previously
                 overlooked numerical optimization model known as the
                 Differential Evolution to expedite the optimization
                 rate of the genetic parameters. To leverage our
                 previous work, we used Gene Expression Programming
                 (GEP) as a benchmark to determine the performance of
                 our proposed method. Nevertheless, E2A can be easily
                 extended to other genetic programming variants. As the
                 experimental results on a wide array of regression
                 problems demonstrate, the E2A method reveals a faster
                 rate of convergence and provides fitter ultimate
                 solutions. However, to further expose the power of the
                 E2A method, we compared it to related methods using
                 self-adaptation previously applied to Genetic
                 Algorithms. Our benchmarking on the same set of
                 regression problems proves the supremacy of our
                 proposed method both in the accuracy and simplicity of
                 the final solutions.",
  notes =        "also known as \cite{4724989}",
}

Genetic Programming entries for Fatemeh Vafaee Weimin Xiao Peter C Nelson Chi Zhou

Citations