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Quantum-Inspired Evolutionary Algorithm for Numerical Optimization
Since they were proposed as an optimization method, evolutionary algorithms (EA) have been used to solve problems in several research fields. This success is due, besides other things, to the factExpand
Fuzzy rule extraction from support vector machines
We propose in this paper a methodology for extracting fuzzy rules from a trained SVM, where the rule's antecedents are associated with fuzzy sets. Expand
Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases
This paper introduces the Inverted Hierarchical Neuro-Fuzzy BSP System (HNFB/sup -1/), a new neuro-fuzzy model that has been specifically created for record classification and rule extraction in databases. Expand
Irregularity detection on low tension electric installations by neural network ensembles
This paper presents the proposal of an intelligent system, composed of two neural networks ensembles, which intends to increase the level of accuracy in the identification of irregularities among low tension consumers. Expand
Fuzzy rules extraction from support vector machines for multi-class classification
This paper proposes a new method for fuzzy rule extraction from trained support vector machines (SVMs) for multi-class problems, named FREx_SVM. Expand
Quantum-Inspired Evolutionary Algorithms applied to numerical optimization problems
This work presents an evolutionary algorithm for numerical optimization problems (Quantum-Inspired Evolutionary Algorithm for Problems based on Numerical Representation — QIEA-R), inspired in the concept of quantum superposition, which allows the optimization process to be carried on with a smaller number of evaluations. Expand
GPF-CLASS: A Genetic Fuzzy model for classification
This work presents a Genetic Fuzzy Classification System (GFCS) which uses the metaheuristic as a way to learn “if-then” fuzzy rules. Expand
Hierarchical neuro-fuzzy quadtree models
Hybrid neuro-fuzzy systems have been in evidence during the past few years, due to its attractive combination of the learning capacity of artificial neural networks with the interpretability of the fuzzy systems. Expand
Data Mining Techniques on the Evaluation of Wireless Churn
This work focuses on one of the most critical issues to plague the wireless telecommunications industry today: the loss of a valuable subscriber to a competitor, also defined as churn. Expand
Well Placement Optimization Using a Genetic Algorithm With Nonlinear Constraints
This paper describes the implementation of a tool, based on a Genetic Algorithm, for the simultaneous optimization of number, location and trajectory of producer and injector wells. Expand