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

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@Article{Parque:2017:Neurocomputing,
  author =       "Victor Parque and Tomoyuki Miyashita",
  title =        "A method to learn high-performing and novel product
                 layouts and its application to vehicle design",
  journal =      "Neurocomputing",
  volume =       "248",
  pages =        "41--56",
  year =         "2017",
  note =         "Neural Networks : Learning Algorithms and
                 Classification Systems",
  ISSN =         "0925-2312",
  DOI =          "doi:10.1016/j.neucom.2016.12.082",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0925231217304332",
  abstract =     "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.",
  keywords =     "genetic algorithms, genetic programming, Design,
                 Vehicle, Optimization, Gradient-free optimization",
}

Genetic Programming entries for Victor Parque Tomoyuki Miyashita

Citations