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Improving Performance of GP by Adaptive Terminal Selection

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PRICAI 2000 Topics in Artificial Intelligence (PRICAI 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1886))

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Abstract

Genetic Programming (GP) is an evolutionary search algorithm which searches a computer program capable of producing the desired solution for a given problem. For the purpose, it is necessary that GP system has access to a set of features that are at least a superset of the features necessary to solve the problem. However, when the feature set given to GP is redundant, GP suffers substantial loss of its efficiency. This paper presents a new approach in GP to acquire relevant terminals from a redundant set of terminals. We propose the adaptive mutation based on terminal weighting mechanism for eliminating irrelevant terminals from the redundant terminal set. We show empirically that the proposed method is effective for finding relevant terminals and improving performance of GP in the experiments on symbolic regression problems.

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References

  1. Avrim L. Blum and Pat Langey.: Selection of relevant features and examples in machine learning. Artificial Intelligence, (1997) Vol 97, 245–271.

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Author information

Authors and Affiliations

  1. Electrotechnical Laboratory(ETL), 1-1-4, Umezono, Tsukuba, Ibaraki, 305-8568, Japan

    Sooyol Ok & Kazuo Miyashita

  2. Institute of Information Sciences and Electronics(IISE), University of Tsukuba, 1-1-1, Tennodai, Tsukuba City, Ibraki, 305-8577, Japan

    Sooyol Ok, Kazuo Miyashita & Seiichi Nishihara

Authors
  1. Sooyol Ok
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  2. Kazuo Miyashita
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  3. Seiichi Nishihara
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Editor information

Editors and Affiliations

  1. Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, Japan

    Riichiro Mizoguchi

  2. Computer Sciences Laboratory, Research School of Information Sciences and Engineering, Australian National University, Canberra, ACT, 0200, Australia

    John Slaney

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© 2000 Springer-Verlag Berlin Heidelberg

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Ok, S., Miyashita, K., Nishihara, S. (2000). Improving Performance of GP by Adaptive Terminal Selection. In: Mizoguchi, R., Slaney, J. (eds) PRICAI 2000 Topics in Artificial Intelligence. PRICAI 2000. Lecture Notes in Computer Science(), vol 1886. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44533-1_45

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  • DOI: https://doi.org/10.1007/3-540-44533-1_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67925-7

  • Online ISBN: 978-3-540-44533-3

  • eBook Packages: Springer Book Archive

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