@Article{Kaylani2009,
  author    = {Kaylani, Assem and Georgiopoulos, Michael and Mollaghasemi, Mansooreh and Anagnostopoulos, Georgios C.},
  title     = {AG-ART: An adaptive approach to evolving ART architectures},
  journal   = {Neurocomputing},
  year      = {2009},
  volume    = {72},
  number    = {10–12},
  pages     = {2079 - 2092},
  month     = {June},
  issn      = {0925-2312},
  note      = {Lattice Computing and Natural Computing (JCIS 2007) / Neural Networks in Intelligent Systems Designn (ISDA 2007)},
  abstract  = {{This paper focuses on classification problems, and in particular
	on the evolution of ARTMAP architectures using genetic algorithms,
	with the objective of improving generalization performance and alleviating
	the adaptive resonance theory (ART) category proliferation problem.
	In a previous effort, we introduced evolutionary fuzzy ARTMAP (FAM),
	referred to as genetic Fuzzy ARTMAP (GFAM). In this paper we apply
	an improved genetic algorithm to FAM and extend these ideas to two
	other ART architectures; ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP
	(GAM). One of the major advantages of the proposed improved genetic
	algorithm is that it adapts the GA parameters automatically, and
	in a way that takes into consideration the intricacies of the classification
	problem under consideration. The resulting genetically engineered
	ART architectures are justifiably referred to as AG-FAM, AG-EAM and
	AG-GAM or collectively as AG-ART (adaptive genetically engineered
	ART). We compare the performance (in terms of accuracy, size, and
	computational cost) of the AG-ART architectures with GFAM, and other
	ART architectures that have appeared in the literature and attempted
	to solve the category proliferation problem. Our results demonstrate
	that AG-ART architectures exhibit better performance than their other
	ART counterparts (semi-supervised ART) and better performance than
	GFAM. We also compare AG-ART's performance to other related results
	published in the classification literature, and demonstrate that
	AG-ART architectures exhibit competitive generalization performance
	and, quite often, produce smaller size classifiers in solving the
	same classification problems. We also show that AG-ART's performance
	gains are achieved within a reasonable computational budget.}},
  doi       = {10.1016/j.neucom.2008.09.016},
  keywords  = {Machine learning},
  owner     = {georgio},
  timestamp = {2012.04.10},
}