CLONAL-GP Framework for Artificial Immune System Inspired Genetic Programming for Classification
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- @InProceedings{DBLP:conf/kes/JabeenB10,
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author = "Hajira Jabeen and Abdul Rauf Baig",
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title = "CLONAL-GP Framework for Artificial Immune System
Inspired Genetic Programming for Classification",
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booktitle = "14th International Conference on Knowledge-Based and
Intelligent Information and Engineering Systems, KES
2010, Part I",
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year = "2010",
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editor = "Rossitza Setchi and Ivan Jordanov and
Robert J. Howlett and Lakhmi C. Jain",
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series = "Lecture Notes in Computer Science",
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volume = "6276",
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pages = "61--68",
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address = "Cardiff",
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month = sep # " 8-10",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-15386-0",
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DOI = "doi:10.1007/978-3-642-15387-7_10",
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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 recognise 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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notes = "KES (1)",
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bibsource = "DBLP, http://dblp.uni-trier.de",
- }
Genetic Programming entries for
Hajira Jabeen
Abdul Rauf Baig
Citations