Evolving Radial Basis Function Networks via GP for Estimating Fitness Values using Surrogate Models
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Kattan:2012:CEC,
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title = "Evolving Radial Basis Function Networks via {GP} for
Estimating Fitness Values using Surrogate Models",
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author = "Ahmed Kattan and Edgar Galvan",
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pages = "3161--3167",
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booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary
Computation",
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year = "2012",
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editor = "Xiaodong Li",
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month = "10-15 " # jun,
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DOI = "doi:10.1109/CEC.2012.6256108",
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address = "Brisbane, Australia",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming,
Surrogate-Assisted Evolutionary Optimisation of
Expensive Problems, Discrete and combinatorial
optimization.",
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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.",
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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