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Analysing BioHEL using challenging boolean functions

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Abstract

In this work we present an extensive empirical analysis of the BioHEL genetics-based machine learning system using the k-Disjunctive Normal Form (k-DNF) family of boolean functions. These functions present a broad set of possible challenges for most machine learning techniques, such as different degrees of specificity, class imbalance and niche overlap. Moreover, as the ideal solutions are known, it is possible to assess if a learning system is able to find them, and how fast. Specifically, we study two aspects of BioHEL: its sensitivity to the coverage breakpoint parameter (that determines the degree of generality pressure applied by the fitness function) and the impact of the default rule policy. The results show that BioHEL is highly sensitive to the choice of coverage breakpoint and that using a default class suitable for the problem allows the system to learn faster than using other default class policies (e.g. the majority class policy). Moreover, the experiments indicate that BioHEL’s scalability depends directly on both k (the specificity of the k-DNF terms) and the number of terms in the problem. In the last part of the paper we discuss alternative policies to adjust the coverage breakpoint parameter.

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Notes

  1. The number of examples needed to learn a classification problem.

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Acknowledgments

The authors would like to thank the UK Engineering and Physical Sciences Research Council (EPSRC) for its support under grant EP/H016597/1. They would also like to acknowledge the High Performance Computing facility at the University of Nottingham for providing the necessary framework for these experiments.

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Authors and Affiliations

  1. ICOS Research Group, School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK

    María A. Franco, Natalio Krasnogor & Jaume Bacardit

  2. Multi-disciplinary Centre for Integrative Biology (MyCIB), School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, UK

    Jaume Bacardit

Authors
  1. María A. Franco
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  2. Natalio Krasnogor
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  3. Jaume Bacardit
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Correspondence to María A. Franco.

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Franco, M.A., Krasnogor, N. & Bacardit, J. Analysing BioHEL using challenging boolean functions. Evol. Intel. 5, 87–102 (2012). https://doi.org/10.1007/s12065-012-0080-9

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