Efficient evolution of accurate classification rules using a combination of gene expression programming and clonal selection

Karakasis, VK; Stafylopatis, A

dc.contributor.author	Karakasis, VK	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T01:28:14Z
dc.date.available	2014-03-01T01:28:14Z
dc.date.issued	2008	en
dc.identifier.issn	1089-778X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18771
dc.subject	Artificial immune systems	en
dc.subject	Clonal selection principle	en
dc.subject	Data mining	en
dc.subject	Gene expression programming	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Antigens	en
dc.subject.other	Bioactivity	en
dc.subject.other	Chemical shift	en
dc.subject.other	Computational linguistics	en
dc.subject.other	Computer programming	en
dc.subject.other	Data mining	en
dc.subject.other	Decision support systems	en
dc.subject.other	Gene expression	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Immunology	en
dc.subject.other	Information management	en
dc.subject.other	Knowledge management	en
dc.subject.other	Magnetic anisotropy	en
dc.subject.other	Artificial immune systems	en
dc.subject.other	Benchmark problems	en
dc.subject.other	Classification rules	en
dc.subject.other	Clonal selection algorithms	en
dc.subject.other	Clonal selection principle	en
dc.subject.other	Clonal Selection Principles	en
dc.subject.other	Clonal selections	en
dc.subject.other	CLONALG	en
dc.subject.other	Data classes	en
dc.subject.other	Data mining tasks	en
dc.subject.other	Evolutionary techniques	en
dc.subject.other	Foreign antigens	en
dc.subject.other	Gene expression programming	en
dc.subject.other	Gene Expression programmings	en
dc.subject.other	Hybrid techniques	en
dc.subject.other	Immune responses	en
dc.subject.other	Immune systems	en
dc.subject.other	Prediction accuracies	en
dc.subject.other	Receptor editing	en
dc.subject.other	Computer software selection and evaluation	en
dc.title	Efficient evolution of accurate classification rules using a combination of gene expression programming and clonal selection	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TEVC.2008.920673	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TEVC.2008.920673	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	A hybrid evolutionary technique is proposed for data mining tasks, which combines a principle inspired by the immune system, namely the clonal selection principle, with a more common, though very efficient, evolutionary technique, gene expression programming (GEP). The clonal selection principle regulates the immune response in order to successfully recognize and confront any foreign antigen, and at the same time allows the amelioration of the immune response across successive appearances of the same antigen. On the other hand, gene expression programming is the descendant of genetic algorithms and genetic programming and eliminates their main disadvantages, such as the genotype-phenotype coincidence, though it preserves their advantageous features. In order to perform the data mining task, the proposed algorithm introduces the notion of a data class antigen, which is used to represent a class of data. the produced rules are evolved by our clonal selection algorithm (CSA), which extends the recently proposed CLONALG algorithm. In CSA, among other new features, a receptor editing step has been incorporated. Moreover, the rules themselves are represented as antibodies that are coded as GEP chromosomes in order to exploit the flexibility and the expressiveness of such encoding. The proposed hybrid technique is tested on a set of benchmark problems in comparison to GEP. In almost all problems considered, the results are very satisfactory and outperform conventional GEP both in terms of prediction accuracy and computational efficiency. © 2008 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Evolutionary Computation	en
dc.identifier.doi	10.1109/TEVC.2008.920673	en
dc.identifier.isi	ISI:000261544100002	en
dc.identifier.volume	12	en
dc.identifier.issue	6	en
dc.identifier.spage	662	en
dc.identifier.epage	678	en