A method to learn high-performing and novel product layouts and its application to vehicle design

Victor Parque*, Tomoyuki Miyashita

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


In this paper we aim at tackling the problem of searching for novel and high-performing product designs. Generally speaking, the conventional schemes usually optimize a (multi) objective function on a dynamic model/simulation, then perform a number of representative real-world experiments to validate and test the accuracy of the some product performance metric. However, in a number of scenarios involving complex product configuration, e.g. optimum vehicle design and large-scale spacecraft layout design, the conventional schemes using simulations and experiments are restrictive, inaccurate and expensive. In this paper, in order to guide/complement the conventional schemes, we propose a new approach to search for novel and high-performing product designs by optimizing not only a proposed novelty metric, but also a performance function which is learned from historical data. Rigorous computational experiments using more than twenty thousand vehicle models over the last thirty years and a relevant set of well-known gradient-free optimization algorithms shows the feasibility and usefulness to obtain novel and high performing vehicle layouts under tight and relaxed search scenarios. The promising results of the proposed method opens new possibilities to build unique and high-performing systems in a wider set of design engineering problems.

Original languageEnglish
Pages (from-to)41-56
Number of pages16
Publication statusPublished - 2017 Jul 26


  • Design
  • Genetic programming
  • Gradient-free optimization
  • Optimization
  • Vehicle

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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