@conference {49, title = {Multi-objective Evolutionary Optimization of Exemplar-based Classifiers: A PNN Test Case}, booktitle = {The 2011 International Joint Conference on Neural Networks (IJCNN{\textquoteright}11) }, year = {2011}, note = {

Acceptance rate 75\% (468/620).

}, publisher = {IEEE}, organization = {IEEE}, address = {San Jose, CA}, abstract = {

In this paper the major principles to effectively design a parameter-less, multi-objective evolutionary algorithm that optimizes a population of probabilistic neural network (PNN) classifier models are articulated; PNN is an example of an exemplar-based classifier. These design principles are extracted from experiences, discussed in this paper, which guided the creation of the parameter-less multi-objective evolutionary algorithm, named MO-EPNN (multi-objective evolutionary probabilistic neural network). Furthermore, these design principles are also corroborated by similar principles used for an earlier design of a parameter-less, multi-objective genetic algorithm used to optimize a population of ART (adaptive resonance theory) models, named MO-GART (multi-objective genetically optimized ART); the ART classifier model is another example of an exemplar-based classifier model. MO-EPNN{\textquoteright}s performance is compared to other popular classifier models, such as SVM (Support Vector Machines) and CART (Classification and Regression Trees), as well as to an alternate competitive method to genetically optimize the PNN. These comparisons indicate that MO-EPNN{\textquoteright}s performance (generalization on unseen data and size) compares favorably to the aforementioned classifier models and to the alternate genetically optimized PNN approach. MO-EPPN{\textquoteright}s good performance, and MO-GART{\textquoteright}s earlier reported good performance, both of whose design relies on the same principles, gives credence to these design principles, delineated in this paper.

}, doi = {10.1109/IJCNN.2011.6033432}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6033432\&tag=1}, author = {Talitha Rubio and Tiantian Zhang and Michael Georgiopoulos and Assem Kaylani} } @article {44, title = {An Adaptive Multiobjective Approach to Evolving ART Architectures}, journal = {Neural Networks, IEEE Transactions on}, volume = {21}, year = {2010}, month = {April}, pages = {529-550}, abstract = {

In this paper, we present the evolution of adaptive resonance theory (ART) neural network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to simultaneously evolve the weights and the topology of three well-known ART architectures; fuzzy ARTMAP (FAM), ellipsoidal ARTMAP (EAM), and Gaussian ARTMAP (GAM). We refer to the resulting architectures as MO-GFAM, MO-GEAM, and MO-GGAM, and collectively as MO-GART. The major advantage of MO-GART is that it produces a number of solutions for the classification problem at hand that have different levels of merit [accuracy on unseen data (generalization) and size (number of categories created)]. MO-GART is shown to be more elegant (does not require user intervention to define the network parameters), more effective (of better accuracy and smaller size), and more efficient (faster to produce the solution networks) than other ART neural network architectures that have appeared in the literature. Furthermore, MO-GART is shown to be competitive with other popular classifiers, such as classification and regression tree (CART) and support vector machines (SVMs).

}, keywords = {adaptive multiobjective approach, adaptive resonance theory, Algorithms, ART architecture, ART neural nets, ART neural network architecture, Artificial Intelligence, ARTMAP, Automated, category proliferation, classification, classification problem, Computer Simulation, ellipsoidal ARTMAP, evolutionary computation, fuzzy ARTMAP, Fuzzy Logic, fuzzy neural nets, Gaussian ARTMAP, genetic algorithms (GAs), genetic operators, Humans, Machine learning, multiobjective evolutionary approach, multiobjective optimization, neural net architecture, Neural Networks (Computer), Normal Distribution, optimisation, pattern recognition}, issn = {1045-9227}, doi = {10.1109/TNN.2009.2037813}, author = {Assem Kaylani and Michael Georgiopoulos and Mansooreh Mollaghasemi and Georgios C. Anagnostopoulos and Christopher Sentelle and Mingyu Zhong} } @conference {43, title = {Multi-objective memetic evolution of ART-based classifiers}, booktitle = {Evolutionary Computation (CEC), 2010 IEEE Congress on}, year = {2010}, note = {

Nominated for Best Paper Award.

Acceptance rate 60\% (622/1027).

}, month = {July}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Barcelona, Spain}, abstract = {

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.

}, keywords = {adaptive 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}, doi = {10.1109/CEC.2010.5586385}, author = {Rong Li and Timothy R. Mersch and Oriana X. Wen and Assem Kaylani and Georgios C. Anagnostopoulos} }