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On using surrogates with genetic programming
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Hildebrandt, Torsten and Branke, Jürgen (2015) On using surrogates with genetic programming. Evolutionary Computation, 23 (3). pp. 343-367. doi:10.1162/EVCO_a_00133 ISSN 1063-6560.
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Official URL: http://dx.doi.org/10.1162/EVCO_a_00133
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.
Item Type: | Journal Article | ||||||
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Divisions: | Faculty of Social Sciences > Warwick Business School > Operational Research & Management Sciences Faculty of Social Sciences > Warwick Business School |
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Journal or Publication Title: | Evolutionary Computation | ||||||
Publisher: | MIT Press | ||||||
ISSN: | 1063-6560 | ||||||
Official Date: | 15 September 2015 | ||||||
Dates: |
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Volume: | 23 | ||||||
Number: | 3 | ||||||
Page Range: | pp. 343-367 | ||||||
DOI: | 10.1162/EVCO_a_00133 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access |
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