Evolving radial basis function networks via GP for estimating fitness values using surrogate models

@article{Kattan2012EvolvingRB,
  title={Evolving radial basis function networks via GP for estimating fitness values using surrogate models},
  author={Ahmed Kattan and Edgar Galv{\'a}n L{\'o}pez},
  journal={2012 IEEE Congress on Evolutionary Computation},
  year={2012},
  pages={1-7}
}
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… CONTINUE READING

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