Machine Learning
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Architecture performance prediction using evolutionary artificial neural networks
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Surrogate model for continuous and discrete genetic optimization based on RBF networks
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Evolving optimal agendas for package deal negotiation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Geometric surrogate-based optimisation for permutation-based problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Geometry of evolutionary algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Geometry of evolutionary algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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In continuous optimisation, Surrogate Models (SMs) are often indispensable components of optimisation algorithms aimed at tackling real-world problems whose candidate solutions are very expensive to evaluate. Because of the inherent spatial intuition behind these models, they are naturally suited to continuous problems but they do not seem applicable to combinatorial problems except for the special case when solutions are naturally encoded as integer vectors. In this paper, we show that SMs can be naturally generalised to encompass combinatorial spaces based in principle on any arbitrarily complex underlying solution representation by generalising their geometric interpretation from continuous to general metric spaces. As an initial illustrative example, we show how Radial Basis Function Networks (RBFNs) can be used successfully as surrogate models to optimise combinatorial problems defined on the Hamming space associated with binary strings.