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