Engineering Evolutionary Intelligent Systems / Edition 1

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Overview

This edited volume deals with the theoretical and methodological aspects, as well as various evolutionary algorithm applications to many real world problems originating from science, technology, business and commerce. It comprises 15 chapters including an introductory chapter which covers the fundamental definitions and outlines some important research challenges. Chapters were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.

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Editorial Reviews

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"The purpose of this book … is to describe the variety of architectures and applications currently being used in evolutionary computation. Evolutionary computation has become an important and frequently used technique for problems in science, engineering, economics, medicine, business, commerce, and the like. … This book could be a useful exposé of how worldwide researchers (outside of the US) are developing new combinations of algorithms … . Summing Up: Recommended. Researchers, faculty, and professionals." (S. E. Haupt, CHOICE, Vol. 46 (01), September, 2008)

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Product Details

  • ISBN-13: 9783642094668
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 11/19/2010
  • Series: Studies in Computational Intelligence Series , #82
  • Edition description: Softcover reprint of hardcover 1st ed. 2008
  • Edition number: 1
  • Pages: 444
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.94 (d)

Table of Contents

Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews.- Genetically Optimized Hybrid Fuzzy Neural Networks: Analysis and Design of Rule-based Multi-layer Perceptron Architectures.- Genetically Optimized Self-organizing Neural Networks Based on Polynomial and Fuzzy Polynomial Neurons: Analysis and Design.- Evolution of Inductive Self-organizing Networks.- Recursive Pattern based Hybrid Supervised Training.- Enhancing Recursive Supervised Learning Using Clustering and Combinatorial Optimization (RSL-CC).- Evolutionary Approaches to Rule Extraction from Neural Networks.- Cluster-wise Design of Takagi and Sugeno Approach of Fuzzy Logic Controller.- Evolutionary Fuzzy Modelling for Drug Resistant HIV-1 Treatment Optimization.- A New Genetic Approach for Neural Network Design.- A Grammatical Genetic Programming Representation for Radial Basis Function Networks.- A Neural-Genetic Technique for Coastal Engineering: Determining Wave-induced Seabed Liquefaction Depth.- On the Design of Large-scale Cellular Mobile Networks Using Multi-population Memetic Algorithms.- A Hybrid Cellular Genetic Algorithm for the Capacitated Vehicle Routing Problem.- Particle Swarm Optimization with Mutation for High Dimensional Problems.

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