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A Metaheuristic Perspective on Learning Classifier Systems

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Metaheuristics for Machine Learning

Part of the book series: Computational Intelligence Methods and Applications ((CIMA))

Abstract

Within this book chapter we summarize Learning Classifier Systems (LCSs), a family of rule-based learning systems with a more than forty-year-long research history, and differentiate LCSs from related approaches like Genetic Programming, Decision Trees, Mixture of Experts as well as Bagging and Boosting. LCS training constructs a finite collection of if-then rules. While the conclusion (the then-part) of each rule is based on a problem-dependent local submodel (e. g.linear regression), the individual conditions and, by extension, the global model structure, are optimized using a—typically evolutionary—metaheuristic. This makes the employed metaheuristic a central part of learning and indispensable for successful training. While most traditional LCSs solely rely on Genetic Algorithms, in this chapter, we also explore systems that employ different metaheuristics while still being similar to LCSs. Furthermore, we discuss the different problems that metaheuristics are solving when applied in this context, for example, discrete or real valued input domains, optimization of individual rule conditions or entire sets of rule conditions, and fitness functions that support nicheing or control bloating. We ascertain that, despite the optimizer being essential, it has been investigated less directly than the learning components so far. Thus, overall, we provide an analysis of LCSs with a focus on the used metaheuristics and present existing solutions as well as current challenges to other practioners in the fields of metaheuristic ML and rule-based learning.

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Notes

  1. 1.

    At the time of this writing, a more formal treatment of the differences is under active investigation.

  2. 2.

    At least as long as the LCS uses the typical binary matching functions. A matching-by-degree LCS can actually be more expressive than a comparable MoE.

  3. 3.

    If matching by degree is used (which is not the case for any of the promiment LCSs), they are softly localized. This means that inputs are somewhat weighted—which goes more in the same direction as boosting.

  4. 4.

    This is equivalent to \((l_{1} \leqslant x_1 < u_{1}) \wedge (l_{2} \leqslant x_2 < u_{2}) \wedge (l_{3} \leqslant x_3 < u_{3})\).

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  1. University of Augsburg, Augsburg, Germany

    Michael Heider, David Pätzel, Helena Stegherr & Jörg Hähner

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  1. Michael Heider
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Correspondence to Michael Heider .

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  1. Qassim University, Buraydah, Saudi Arabia

    Mansour Eddaly

  2. Abu Dhabi Women Campus, Higher Colleges of Technology, Abu Dhabi, United Arab Emirates

    Bassem Jarboui

  3. Paris-Est Créteil University, Paris, France

    Patrick Siarry

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Heider, M., Pätzel, D., Stegherr, H., Hähner, J. (2023). A Metaheuristic Perspective on Learning Classifier Systems. In: Eddaly, M., Jarboui, B., Siarry, P. (eds) Metaheuristics for Machine Learning. Computational Intelligence Methods and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-19-3888-7_3

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