Kattan, Ahmed and Galvan, Edgar (2012) Evolving radial basis function networks via GP for estimating fitness values using surrogate models. IEEE Transactions on Evolutionary Computation. pp. 1-7. ISSN 1089-778X

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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.

Item Type:	Article
Keywords:	Training; Mathematical model; Computational modeling; Standards; Approximation methods; Search problems; Optimization;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15384
Identification Number:	https://doi.org/10.1109/CEC.2012.6256108
Depositing User:	Edgar Galvan
Date Deposited:	01 Feb 2022 12:28
Journal or Publication Title:	IEEE Transactions on Evolutionary Computation
Publisher:	IEEE
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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