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Local Landscape Patterns for Fitness Landscape Analysis

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Book cover Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

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Abstract

Almost all problems targeted by evolutionary computation are black-box or heavily complex, and their fitness landscapes usually are unknown. Selection of the appropriate search algorithm and parameters is a crucial topic when the landscape of a given target problem could be unknown in advance. Although several landscape features have been proposed in this context, examining a variety of landscape features is useful for problem understanding. In this paper, we propose a novel feature vector for characterizing the fitness landscape using the local landscape patterns (LLP). The proposed feature vector is composed by the histogram of the fitness patterns of the local candidate solutions. We extract the proposed LLP feature vector from well-known continuous optimization benchmark functions and BBOB 2013 benchmark set to investigate the properties of the proposed landscape feature and discuss about its effectiveness.

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Author information

Authors and Affiliations

  1. College of Science and Engineering, Aoyama Gakuin University, Sagamihara, Kanagawa, Japan

    Shinichi Shirakawa

  2. Graduate School of Environment and Information Sciences, Yokohama National University, Yokohama, Kanagawa, Japan

    Tomoharu Nagao

Authors
  1. Shinichi Shirakawa
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  2. Tomoharu Nagao
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Shirakawa, S., Nagao, T. (2014). Local Landscape Patterns for Fitness Landscape Analysis. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_40

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_40

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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