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Learning the Optimal Product Design Through History

  • Victor ParqueEmail author
  • Tomoyuki Miyashita
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)

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 high-performing 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.

Keywords

Design Vehicle Optimization Genetic programming Particle swarm 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Modern Mechanical EngineeringWaseda UniversityShinjuku-kuJapan

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