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A New Multiobjective Genetic Programming for Extraction of Design Information from Non-dominated Solutions

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Evolutionary Multi-Criterion Optimization (EMO 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7811))

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Abstract

We propose a new type of multi-objective genetic programming (MOGP) for multi-objective design exploration (MODE). The characteristic of the new MOGP is the simultaneous symbolic regression to multiple objective functions using correlation coefficients. This methodology is applied to non-dominated solutions of the multi-objective design optimization problem to extract information between objective functions and design parameters. The result of MOGP is symbolic equations that are highly correlated to each objective function through a single GP run. These equations are also highly correlated to several objective functions. The results indicate that the proposed MOGP is capable of finding new design parameters more closely related to the objective functions than the original design parameters. The proposed MOGP is applied to the test problem and the practical design problem to evaluate the capability.

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Author information

Authors and Affiliations

  1. Japan Aerospace Exploration Agency, Institute of Space and Astronautical Science, 3-1-1 Yoshinodai, Sagamihara, Kanagawa, 252-5210, Japan

    Tomoaki Tatsukawa, Taku Nonomura, Akira Oyama & Kozo Fujii

Authors
  1. Tomoaki Tatsukawa
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  2. Taku Nonomura
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  3. Akira Oyama
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  4. Kozo Fujii
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Editor information

Editors and Affiliations

  1. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK

    Robin C. Purshouse

  2. Department of Automatic Control Systems and Engineering, University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK

    Peter J. Fleming

  3. Department of Informatics Engineering, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290, Coimbra, Portugal

    Carlos M. Fonseca

  4. Department of Economics and Business, University of Catania, Corso Italia 55, 95129, Catania, Italy

    Salvatore Greco

  5. Unilever Research & Development Port Sunlight, Quarry Road East, CH63 3JW, Merseyside, UK

    Jane Shaw

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© 2013 Springer-Verlag Berlin Heidelberg

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Tatsukawa, T., Nonomura, T., Oyama, A., Fujii, K. (2013). A New Multiobjective Genetic Programming for Extraction of Design Information from Non-dominated Solutions. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds) Evolutionary Multi-Criterion Optimization. EMO 2013. Lecture Notes in Computer Science, vol 7811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37140-0_40

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  • DOI: https://doi.org/10.1007/978-3-642-37140-0_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37139-4

  • Online ISBN: 978-3-642-37140-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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