Linear Combination of Distance Measures for Surrogate Models in Genetic Programming

By Martin Zaefferer, Jörg Stork, Oliver Flasch, Thomas Bartz-Beielstein. 2018

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Surrogate models are a well established approach to reduce the number of expensive function evaluations in continuous optimization. In the context of genetic programming, surrogate modeling still poses a challenge, due to the complex genotype-phenotype relationships. We investigate how different genotypic and phenotypic distance measures can be used to learn Kriging models as surrogates. We compare the measures and suggest to use their linear combination in a kernel.

Subfields: Artificial Intelligence, Computational Theory and Mathematics