ABSTRACT Resumen En este artículo se presenta un nuevo elemento poblacional a considerar en el diseño de algoritmos evolutivos multiobjetivo para la optimización de Redes de Funciones de Base Radial. Concretamente, se divide la población en subpoblaciones virtuales, donde cada subpoblación está compuesta por individuos o redes con el mismo número de neuronas. El objetivo será mantener el mejor individuo (individuo élite) de cada subpoblación entre generaciones, consiguiendo aumentar la diversidad de la población. Este elemento se implementa en el algoritmo EMORBFN, ya presentado por los autores, y se aplica a la tarea de clasificación. En los resultados se puede observar cómo la introducción de este nuevo elemento mejora el comportamiento general del algoritmo. Palabras Clave: Diseño Multiobjetivo, Redes de Funciones de Base Radial, Elitismo.

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    ABSTRACT: Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete
    IEEE Transactions on Evolutionary Computation 05/1997; · 4.81 Impact Factor
  • Complex Systems. 01/1988; 2:321-355.
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    ABSTRACT: In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.
    IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 11/2005; 35(5):928-47. · 3.24 Impact Factor


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