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Building Optimal Committees of Genetic Programs

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Parallel Problem Solving from Nature PPSN VI (PPSN 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1917))

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Abstract

Committee machines are known to improve the performance of individual learners. Evolutionary algorithms generate multiple individuals that can be combined to build committee machines. However, it is not easy to decide how big the committee should be and what members constitute the best committee. In this paper, we present a probabilistic search method for determining the size and members of the committees of individuals that are evolved by a standard GP engine. Applied to a suite of benchmark learning tasks, the GP committees achieved significant improvement in prediction accuracy.

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Author information

Authors and Affiliations

  1. Artificial Intelligence Lab (SCAI) School of Computer Science and Engineering, Seoul National University, Seoul, 151-742, Korea

    Byoung-Tak Zhang & Je-Gun Joung

Authors
  1. Byoung-Tak Zhang
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  2. Je-Gun Joung
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Editor information

Editors and Affiliations

  1. CMAP, Ecole Polytechnique, 91128, Palaiseau Cedex, France

    Marc Schoenauer

  2. Dept. of Mechanical Engineering Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology Kanpur, Kanpur, Pin, 208 016, India

    Kalyanmoy Deb

  3. Fachbereich Informatik, Lehrstuhl für Systemanalyse, Universität Dortmund, Joseph-von-Fraunhofer-Str. 20, 44221, Dortmund, Germany

    Günther Rudolph  & Hans-Paul Schwefel  & 

  4. School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Xin Yao

  5. Projet Fractales, INRIA Rocquencourt, BP 105, 78153, Le Chesnay Cedex, France

    Evelyne Lutton

  6. Dept. de Arquitectura y Technologa de los Computadores, GeNeura Team, Universidad de Granada, Campus Fuenetenueva, s/n, Granada

    Juan Julian Merelo

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© 2000 Springer-Verlag Berlin Heidelberg

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Zhang, BT., Joung, JG. (2000). Building Optimal Committees of Genetic Programs. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_23

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  • DOI: https://doi.org/10.1007/3-540-45356-3_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41056-0

  • Online ISBN: 978-3-540-45356-7

  • eBook Packages: Springer Book Archive

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