Evolving Radial Basis Function Networks via GP for Estimating Fitness Values using Surrogate Models

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Kattan:2012:CEC,
  title =        "Evolving Radial Basis Function Networks via {GP} for
                 Estimating Fitness Values using Surrogate Models",
  author =       "Ahmed Kattan and Edgar Galvan",
  pages =        "3161--3167",
  booktitle =    "Proceedings of the 2012 IEEE Congress on Evolutionary
                 Computation",
  year =         "2012",
  editor =       "Xiaodong Li",
  month =        "10-15 " # jun,
  DOI =          "doi:10.1109/CEC.2012.6256108",
  address =      "Brisbane, Australia",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming,
                 Surrogate-Assisted Evolutionary Optimisation of
                 Expensive Problems, Discrete and combinatorial
                 optimization.",
  abstract =     "In real-world problems with candidate solutions that
                 are very expensive to evaluate, Surrogate Models (SMs)
                 mimic the behaviour of the simulation model as closely
                 as possible while being computationally cheaper to
                 evaluate. Due to their nature, SMs can be seen as
                 heuristics that can help to estimate the fitness of a
                 candidate solution without having to evaluate it. In
                 this paper, we propose a new SM based on genetic
                 programming (GP) and Radial Basis Function Networks
                 (RBFN), called GP-RBFN Surrogate. More specifically, we
                 use GP to evolve both: the structure of a RBF and its
                 parameters. The SM evolved by our algorithm is tested
                 in one of the most studied NP-complete problem
                 (MAX-SAT) and its performance is compared against RBFN
                 Surrogate, GAs, Random Search and (1+1) ES. The results
                 obtained by performing extensive empirical experiments
                 indicate that our proposed approach outperforms the
                 other four methods in terms of finding better solutions
                 without the need of evaluating a large portion of
                 candidate solutions.",
  notes =        "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
                 EPS and the IET.",
}

Genetic Programming entries for Ahmed Kattan Edgar Galvan Lopez

Citations