Learning the optimal product design through history

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

    4 Citations (Scopus)

    Abstract

    The search for novel and high-performing product designs is a ubiquitous problem in science and engineering: aided by advances in optimization methods the conventional approaches usually optimize a (multi) objective function using simulations followed by experiments. However, in some scenarios such as vehicle layout design, simulations and experiments are restrictive, inaccurate and expensive. In this paper, we propose an alternative approach to search for novel and highperforming product designs by optimizing not only a proposed novelty metric, but also a performance function learned from historical data. Computational experiments using more than twenty thousand vehicle models over the last thirty years shows the usefulness and promising results for a wider set of design engineering problems.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    PublisherSpringer Verlag
    Pages382-389
    Number of pages8
    Volume9489
    ISBN (Print)9783319265315
    DOIs
    Publication statusPublished - 2015
    Event22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey
    Duration: 2015 Nov 92015 Nov 12

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9489
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other22nd International Conference on Neural Information Processing, ICONIP 2015
    CountryTurkey
    CityIstanbul
    Period15/11/915/11/12

    Keywords

    • Design
    • Genetic programming
    • Optimization
    • Particle swarm
    • Vehicle

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Fingerprint Dive into the research topics of 'Learning the optimal product design through history'. Together they form a unique fingerprint.

  • Cite this

    Parque Tenorio, V., & Miyashita, T. (2015). Learning the optimal product design through history. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9489, pp. 382-389). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9489). Springer Verlag. https://doi.org/10.1007/978-3-319-26532-2_42