Recent Advances In Simulated Evolution And Learning

By: Kay Chen Tan (Editor), Meng Hiot Lim (Editor), Xin Yao (Editor), Lipo Wang (Editor)

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Hardcover | 27 August 2004

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832 Pages

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22.86 x 15.88 x 4.45

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Inspired by the Darwinian framework of evolution through natural selection and adaptation, the field of evolutionary computation has been growing very rapidly, and is today involved in many diverse application areas. This book covers the latest advances in the theories, algorithms, and applications of simulated evolution and learning techniques. It provides insights into different evolutionary computation techniques and their applications in domains such as scheduling, control and power, robotics, signal processing, and bioinformatics. The book will be of significant value to all postgraduates, research scientists and practitioners dealing with evolutionary computation or complex real-world problems.

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.,." covers a broad range of evolutionary computation topics in sufficient depth to be useful to both researchers and practitioners ..."

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Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Machine Learning Neural Networks & Fuzzy Systems Booktopia Publisher Services World Scientific Publishing Systems Analysis & Design

Co-evolutionary learning in strategic environments	p. 1
Using evolution to learn user preferences	p. 20
A parallel genetic algorithm for clustering	p. 41
Using SIMD genetic programming for fault-tolerant trading strategies	p. 61
An efficient coevolutionary algorithm based on merging and splitting of species	p. 78
Schema analysis of genetic algorithms on multiplicative landscape	p. 93
Evolutionary learning strategies for artificial life characters	p. 112
Adaptive strategy for GA inspired by biological evolution	p. 132
The influence of stochastic quality functions on evolutionary search	p. 152
Theoretical analysis of the GA performance with a multiplicative royal road function	p. 173
A real-coded cellular genetic algorithm inspired by predator-prey interactions	p. 191
Observed dynamics of large scale parallel evolutionary algorithms with implications for protein engineering	p. 208
Using edge histogram models to solve flow shop scheduling problems with probabilistic model-building genetic algorithms	p. 230
Collective movements of mobile robots with behavior models of a fish	p. 250
Automatic modularization with speciated neural network ensemble	p. 268
Search engine development using evolutionary computation methodologies	p. 284
Evaluating evolutionary multi-objective optimization algorithms using running performance metrics	p. 307
Visualization technique for analyzing non-dominant pareto optimality	p. 327
Image classification using particle swarm optimization	p. 347
A coevolutionary genetic search for a layout problem	p. 366
Sensitivity analysis in multi-objective evolutionary design	p. 386
Integrated production and transportation scheduling in supply chain optimisation	p. 406
Evolution of fuzzy rule based controllers for dynamic environments	p. 426
Applications of evolution algorithms to the synthesis of single/dual-rail mixed PTL/static logic for low-power applications	p. 446
Evolutionary multi-objective robotics : evolving a physically simulated quadruped using the PDE algorithm	p. 466
Applying Bayesian networks in practical customer satisfaction studies	p. 486
An adaptive length chromosome hyper-heuristic genetic algorithm for a trainer scheduling problem	p. 506
Design optimization of permanent magnet synchronous machine using genetic algorithms	p. 526
A genetic algorithm for joint optimization of spare capacity and delay in self-healing network	p. 542
Optimization of DS-CDMA code sequences for wireless systems	p. 562
An efficient evolutionary algorithm for multicast routing with multiple QoS constraints	p. 581
Constrained optimization of multilayered anti-reflection coatings using genetic algorithms	p. 603
Sequential construction of features based on genetically transformed data	p. 623
Refrigerant leak prediction in supermarkets using evolved neural networks	p. 643
Worst-case instances and lower bounds via genetic algorithms	p. 662
Prediction of protein secondary structure by multi-modal neural network	p. 682
Table of Contents provided by Blackwell. All Rights Reserved.

Recent Advances In Simulated Evolution And Learning

At a Glance

Hardcover

Industry Reviews

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How to return your order

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