title: On Using Surrogates with Genetic Programming.
abstract: One way to accelerate evolutionary algorithms with expensive fitness evaluations is to combine them with surrogate models. Surrogate models are efficiently computable approximations of the fitness function, derived by means of statistical or machine learning techniques from samples of fully evaluated solutions. But these models usually require a numerical representation, and therefore cannot be used with the tree representation of genetic programming (GP). In this paper, we present a new way to use surrogate models with GP. Rather than using the genotype directly as input to the surrogate model, we propose using a phenotypic characterization. This phenotypic characterization can be computed efficiently and allows us to define approximate measures of equivalence and similarity. Using a stochastic, dynamic job shop scenario as an example of simulation-based GP with an expensive fitness evaluation, we show how these ideas can be used to construct surrogate models and improve the convergence speed and solution quality of GP.
authors: Bremer Institut für Produktion und Logistik GmbH (BIBA) an der Universität Bremen, Bremen, Germany hil@biba.uni-bremen.de.
authors: Torsten
authors: T
authors: Hildebrandt
authors: Warwick Business School, University of Warwick, CV4 7AL Coventry, UK Juergen.Branke@wbs.ac.uk.
authors: Jürgen
authors: J
authors: Branke
mesh_terms: D000465:Algorithms; D019143:Evolution, Molecular; D008962:Models, Theoretical
keywords: Genetic programming; phenotypic characterization; surrogates