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Investigation of a Cellular Genetic Algorithm that Mimics Landscape Ecology

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Simulated Evolution and Learning (SEAL 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1585))

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Abstract

The cellular genetic algorithm (CGA) combines GAs with cellular automata by spreading an evolving population across a pseudo-landscape. In this study we use insights from ecology to introduce new features, such as disasters and connectivity changes, into the algorithm. We investigate the performance and behaviour of the algorithm on standard GA hard problems. The CGA has the advantage of avoiding premature convergence and outperforms standard GAs on particular problems. A potentially important feature of the algorithm’s behaviour is that the fitness of solutions frequently improves in large jumps following disturbances (culling by patches).

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Author information

Authors and Affiliations

  1. School of Environmental and Information Sciences, Charles Sturt University, PO Box 789, Albury, New South Wales, 2640, Australia

    Michael Kirley, Xiaodong Li & David G. Green

Authors
  1. Michael Kirley
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  2. Xiaodong Li
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  3. David G. Green
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Editor information

Editors and Affiliations

  1. School of Computer Science, University College, UNSW Australian Defence Force Academy, Canberra, ACT, Australia, 2600

    Bob McKay , Xin Yao  & Charles S. Newton ,  & 

  2. Department of Electrical Engineering Korea Advanced Institute of Science and Technology, 373-1, Kusung-dong, Yusung-gu, Taejon-shi, 305-701, Korea

    Jong-Hwan Kim

  3. Department of Information Electronics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan

    Takeshi Furuhashi

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© 1999 Springer-Verlag Berlin Heidelberg

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Kirley, M., Li, X., Green, D.G. (1999). Investigation of a Cellular Genetic Algorithm that Mimics Landscape Ecology. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_13

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  • DOI: https://doi.org/10.1007/3-540-48873-1_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65907-5

  • Online ISBN: 978-3-540-48873-6

  • eBook Packages: Springer Book Archive

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