Authors:
Cleber Zanchettin
and
Teresa B. Ludermir
Affiliation:
Center for Informatics, Federal University of Pernambuco, Brazil
Keyword(s):
Global optimization, Artificial neural networks, Relevant feature selection, Experimental design.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Programming
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial Applications of Artificial Intelligence
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
In this paper a global and local optimization method is presented. This method is based on the integration of the heuristic Simulated Annealing, Tabu Search, Genetic Algorithms and Backpropagation. The performance of the method is investigated in the optimization of Multi-layer Perceptron artificial neural network architecture and weights. The heuristics perform the search in a constructive way and based on the pruning of irrelevant connections among the network nodes. Experiments demonstrated that the method can also be used for relevant feature selection. Experiments are performed with four classification and one prediction datasets.