On vehicle surrogate learning with genetic programming ensembles

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
出版社Association for Computing Machinery, Inc
ページ1704-1710
ページ数7
ISBN(電子版)9781450357647
DOI
出版ステータスPublished - 2018 7 6
イベント2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
継続期間: 2018 7 152018 7 19

出版物シリーズ

名前GECCO 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

ASJC Scopus subject areas

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

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