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Multi-objective memetic evolution of ART-based classifiers

TitleMulti-objective memetic evolution of ART-based classifiers
Publication TypeConference Paper
Year of Publication2010
AuthorsLi R, Mersch TR, Wen OX, Kaylani A, Anagnostopoulos GC
Conference NameEvolutionary Computation (CEC), 2010 IEEE Congress on
Date PublishedJuly
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Conference LocationBarcelona, Spain
Keywordsadaptive resonance theory, ART classifiers, ART-based classification models, ART-based classifiers, classification error, Complexity theory, Cooling, Electronic mail, fuzzy set theory, genetic algorithms, interacting subpopulations, Mathematical model, MO-GART framework, MOME-ART, multiobjective memetic evolutionary framework, Pareto front quality, Pareto optimisation, parsimony property, pattern classification, semisupervised fuzzy ARTMAP, simulated annealing, simulated annealing process, solution refinement, structural complexity, Subspace constraints, Training, training framework

In this paper we present a novel framework for evolving ART-based classification models, which we refer to as MOME-ART. The new training framework aims to evolve populations of ART classifiers to optimize both their classification error and their structural complexity. Towards this end, it combines the use of interacting sub-populations, some traditional elements of genetic algorithms to evolve these populations and a simulated annealing process used for solution refinement to eventually give rise to a multi-objective, memetic evolutionary framework. In order to demonstrate its capabilities, we utilize the new framework to train populations of semi-supervised Fuzzy ARTMAP and compare them with similar networks trained via the recently published MO-GART framework, which has been shown as being very effective in yielding high-quality ART-based classifiers. The experimental results show clear advantages of MOME-ART in terms of Pareto Front quality and density, as well as parsimony properties of the resulting classifiers.


Nominated for Best Paper Award.

Acceptance rate 60% (622/1027).


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