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

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完整

A New Genetic Approach for Neural Network Design,y-inspired computational models that use evolutionary algorithms (EAs) in conjunction with neural networks (NNs) to solve problems. EAs are based on natural genetic evolution of individuals in a defined environment and they are useful for complex optimization problems with huge number of parameters

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resuscitation

amphibian

淘气

A Hybrid Cellular Genetic Algorithm for the Capacitated Vehicle Routing Problem,idual can only interact with its neighbors. The existence of small overlapped neighborhoods in this decentralized population provides both diversity and opportunities for exploration, while the exploitation of the search space is strengthened inside each neighborhood. This balance between intensific

拖债

PAN

Genetically Optimized Hybrid Fuzzy Neural Networks: Analysis and Design of Rule-based Multi-layer P learning algorithm and genetic optimization. We distinguish between two types of the fuzzy rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general opt

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伸展

Evolution of Inductive Self-organizing Networks,osome is computed. The appropriate information of each node is evolved accordingly and tuned gradually throughout the EA iterations. The evolved network is a sophisticated and versatile architecture, which can construct models from a limited data set as well as from poorly defined complex problems.