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Balancing Parent and Offspring Selection in Genetic Programming

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AI 2009: Advances in Artificial Intelligence (AI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5866))

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Abstract

In order to drive Genetic Programming (GP) search towards an optimal situation, balancing selection pressure between the parent and offspring selection phases is an important aspect and very challenging. Our previous work showed that stochastic elements cannot be removed from both parent and offspring selections and suggested that maximising diversity in parents and minimising randomness in offspring could provide significantly good performance. This paper conducts additional carefully designed experiments to further investigate how diverse the parent should be if the offspring selection pressure is intensive. This paper shows that any attempt on adding more selection pressure to the parent selection can result in lower GP performance, and the higher the parent selection pressure, the worse the GP performance. The results confirm and strengthen the finding in our previous work.

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Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, New Zealand

    Huayang Xie & Mengjie Zhang

Authors
  1. Huayang Xie
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  2. Mengjie Zhang
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Editor information

Editors and Affiliations

  1. Clayton School of Information Technology, Monash University, 3800, Clayton, VIC, Australia

    Ann Nicholson

  2. School of Computer Science and Information Technology, RMIT University, 3001, Melbourne, VIC, Australia

    Xiaodong Li

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Xie, H., Zhang, M. (2009). Balancing Parent and Offspring Selection in Genetic Programming. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_46

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  • DOI: https://doi.org/10.1007/978-3-642-10439-8_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10438-1

  • Online ISBN: 978-3-642-10439-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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