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CLONAL-GP Framework for Artificial Immune System Inspired Genetic Programming for Classification

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Knowledge-Based and Intelligent Information and Engineering Systems (KES 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6276))

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Abstract

This paper presents a novel framework for artificial immune system (AIS) inspired evolution in Genetic Programming (GP). A typical GP system uses the reproduction operators mimicking the phenomena of natural evolution to search for efficient classifiers. The proposed framework uses AIS inspired clonal selection algorithm to evolve classifiers using GP. The clonal selection principle states that, in human immune system, high affinity cells that recognize the invading antigens are selected to proliferate. Furthermore, these cells undergo hyper mutation and receptor editing for maturation. In this paper, we propose a computational implementation of the clonal selection principle. The motivation for using non-Darwinian evolution includes avoidance of bloat, training time reduction and simpler classifiers. We have performed empirical analysis of proposed framework over a benchmark dataset from UCI repository. The CLONAL-GP is contrasted with two variants of GP based classification mechanisms and results are found encouraging.

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Author information

Authors and Affiliations

  1. National University of Computer and Emerging Sciences, Islamabad, Pakistan

    Hajira Jabeen & Abdul Rauf Baig

Authors
  1. Hajira Jabeen
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  2. Abdul Rauf Baig
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Editor information

Editors and Affiliations

  1. School of Engineering, The Parade, Cardiff University, CF24 3AA, Cardiff, UK

    Rossitza Setchi

  2. Dept. of Computer Science and Software Engineering, BUckingham Building, Lion Terrace, University of Portsmouth, PO1 3HE, Portsmouth, UK

    Ivan Jordanov

  3. KES International, 145-157, St. John Street, EC1V 4PY, London, UK

    Robert J. Howlett

  4. School of Electrical and Information Engineering, University of South Australia, ,, Adelaide, Mawson Lakes Campus, 5095, SA, Australia

    Lakhmi C. Jain

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Jabeen, H., Baig, A.R. (2010). CLONAL-GP Framework for Artificial Immune System Inspired Genetic Programming for Classification. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15387-7_10

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  • DOI: https://doi.org/10.1007/978-3-642-15387-7_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15386-0

  • Online ISBN: 978-3-642-15387-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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