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Central Point Crossover for Neuro-genetic Hybrids

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Genetic and Evolutionary Computation – GECCO 2004 (GECCO 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3102))

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Abstract

In this paper, we consider each neural network as a point in a multi-dimensional problem space and suggest a crossover that locates the central point of a number of neural networks. By this, genetic algorithms can spend more time around attractive areas. We also apply representational normalization to neural networks to maintain genotype consistency in crossover. For the normalization, we utilize the Hungarian method of matching problems. The experimental results of our neuro-genetic algorithm overall showed better performance over the traditional multi-start heuristic and the genetic algorithm with a traditional crossover. These results are evidence that it is attractive to exploit central areas of local optima.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Seoul National University, Shillim-dong, Kwanak-gu, Seoul, 151-742, Korea

    Soonchul Jung & Byung-Ro Moon

Authors
  1. Soonchul Jung
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  2. Byung-Ro Moon
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Editor information

Editors and Affiliations

  1. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

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© 2004 Springer-Verlag Berlin Heidelberg

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Jung, S., Moon, BR. (2004). Central Point Crossover for Neuro-genetic Hybrids. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24854-5_124

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  • DOI: https://doi.org/10.1007/978-3-540-24854-5_124

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22344-3

  • Online ISBN: 978-3-540-24854-5

  • eBook Packages: Springer Book Archive

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