Titlebook: Engineering Evolutionary Intelligent Systems; Ajith Abraham,Crina Grosan,Witold Pedrycz Book 2008 Springer-Verlag Berlin Heidelberg 2008 E

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Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews,olving complexity, noisy environment, imprecision, uncertainty and vagueness. In this Chapter, we illustrate the various possibilities for designing intelligent systems using evolutionary algorithms and also present some of the generic evolutionary design architectures that has evolved during the la

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Genetically Optimized Hybrid Fuzzy Neural Networks: Analysis and Design of Rule-based Multi-layer Pmethodology supporting their construction. A series of of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). T

CANE

Genetically Optimized Self-organizing Neural Networks Based on Polynomial and Fuzzy Polynomial Neurptimized multilayer perceptron with polynomial neurons (PNs) or fuzzy polynomial neurons (FPNs), develop a comprehensive design methodology involving mechanisms of genetic optimization and carry out a series of numeric experiments. The conventional SONN is based on a self-organizing and an evolution

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Evolution of Inductive Self-organizing Networks,zing network dwells on the idea of group method of data handling. The performances of the network depend strongly on the number of input variables available to the model, the number of input variables, and type (order) of the polynomials to each node. They must be fixed by the designer in advance be

Intractable

Recursive Pattern based Hybrid Supervised Training,pseudo global optimal solutions to evolve a set of neural networks, each of which can solve correctly a subset of patterns. The pattern-based algorithm uses the topology of training and validation data patterns to find a set of pseudo-optima, each learning a subset of patterns. It is therefore well

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