On vehicle surrogate learning with genetic programming ensembles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Learning surrogates for product design and optimization is potential to capitalize on competitive market segments. In this paper we propose an approach to learn surrogates of product performance from historical clusters by using ensembles of Genetic Programming. By using computational experiments involving more than 500 surrogate learning instances and 27,858 observations of vehicle models collected over the last thirty years shows (1) the feasibility to learn function surrogates as symbolic ensembles at different levels of granularity of the hierarchical vehicle clustering, (2) the direct relationship of the predictive ability of the learned surrogates in both seen (training) and unseen (testing) scenarios as a function of the number of cluster instances, and (3) the attractive predictive ability of relatively smaller ensemble of trees in unseen scenarios. We believe our approach introduces the building blocks to further advance on studies regarding data-driven product design and market segmentation.

Original languageEnglish
Title of host publicationGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages1704-1710
Number of pages7
ISBN (Electronic)9781450357647
DOIs
Publication statusPublished - 2018 Jul 6
Event2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
Duration: 2018 Jul 152018 Jul 19

Publication series

NameGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

Other

Other2018 Genetic and Evolutionary Computation Conference, GECCO 2018
CountryJapan
CityKyoto
Period18/7/1518/7/19

Keywords

  • Genetic Programming
  • Surrogate Function
  • Vehicle Clusters

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computational Theory and Mathematics
  • Theoretical Computer Science

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  • Cite this

    Parque, V., & Miyashita, T. (2018). On vehicle surrogate learning with genetic programming ensembles. In GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion (pp. 1704-1710). (GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3205651.3208310