Paper Info
Title
CLONAL-GP framework for artificial immune system inspired genetic programming for classification
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.
Year
DOI
Venue
2010
10.1007/978-3-642-15387-7_10
KES (1)
Keywords
Field
DocType
typical gp system,proposed framework,human immune system,non-darwinian evolution,artificial immune system,genetic programming,clonal selection principle state,clonal selection algorithm,clonal-gp framework,clonal selection principle,natural evolution,inspired evolution,classification,artificial immune systems
Artificial immune system,Antigen,Computer science,Genetic programming,Immune system,Artificial intelligence,Clonal selection algorithm,Clonal selection,Receptor editing,Mutation
Conference
Volume
ISSN
ISBN
6276
0302-9743
3-642-15386-0
Citations 
PageRank 
References 
0
0.34
9
Authors
2
Name
Order
Citations
PageRank
Hajira Jabeen16710.58
Abdul Rauf Baig212615.82