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MOLeCS: Using Multiobjective Evolutionary Algorithms for Learning

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Book cover Evolutionary Multi-Criterion Optimization (EMO 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1993))

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Abstract

MOLeCS is a classifier system (CS) which addresses its learning as a multiobjective task. Its aim is to develop an optimal set of rules, optimizing the accuracy and the generality of each rule simultaneously. This is achieved by considering these two goals in the rule fitness. The paper studies four multiobjective strategies that establish a compromise between accuracy and generality in different ways. The results suggest that including the decision maker’s preferences in the search process improves the overall performance of the obtained rule set. The paper also studies a third major objective: covering (the maintenance of a set of different rules solving together the learning problem), through different niching mechanisms. After a performance analysis using some benchmark problems, MOLeCS is applied to a real-world categorization task: the diagnosis of breast cancer.

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Author information

Authors and Affiliations

  1. Computer Science Department Enginyeria i Arquitectura La Salle, Ramon Llull University, Passeig Bonanova, 8, 08022, Barcelona, Spain

    Ester Bernadó i Mansilla & Josep M. Garrell i Guiu

Authors
  1. Ester Bernadó i Mansilla
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  2. Josep M. Garrell i Guiu
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, ETH Zentrum, Gloriastrasse 35, 8092, Zurich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

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

    Kalyanmoy Deb

  3. Electrical Engineering Department,Computer Science Section Av.Instituto Politecnico Nacional No.2508, CINVESTAV-IPN, Col.San Pedro Zacatenco, 07300, Mexico City, Mexico

    Carlos Artemio Coello Coello

  4. Department of Computer Science, University of Reading, P.O.Box 225, Reading RG6 6AY, Whiteknights, UK

    David Corne

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© 2001 Springer-Verlag Berlin Heidelberg

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Bernadó i Mansilla, E., Garrell i Guiu, J.M. (2001). MOLeCS: Using Multiobjective Evolutionary Algorithms for Learning. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_49

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  • DOI: https://doi.org/10.1007/3-540-44719-9_49

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41745-3

  • Online ISBN: 978-3-540-44719-1

  • eBook Packages: Springer Book Archive

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