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Handbook of Global Optimization

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Cover of 'Handbook of Global Optimization'

Table of Contents

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    Book Overview
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    Chapter 1 Tight Relaxations for Nonconvex Optimization Problems Using the Reformulation-Linearization/Convexification Technique (RLT)
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    Chapter 2 Exact Algorithms for Global Optimization of Mixed-Integer Nonlinear Programs
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    Chapter 3 Algorithms for Global Optimization and Discrete Problems Based on Methods for Local Optimization
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    Chapter 4 An Introduction to Dynamical Search
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    Chapter 5 Two-Phase Methods for Global Optimization
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    Chapter 6 Simulated Annealing Algorithms for Continuous Global Optimization
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    Chapter 7 Stochastic Adaptive Search
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    Chapter 8 Implementation of Stochastic Adaptive Search with Hit-and-Run as a Generator
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    Chapter 9 Genetic Algorithms
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    Chapter 10 Dataflow Learning in Coupled Lattices: An Application to Artificial Neural Networks
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    Chapter 11 Taboo Search: An Approach to the Multiple-Minima Problem for Continuous Functions
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    Chapter 12 Recent Advances in the Direct Methods of X-Ray Crystallography
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    Chapter 13 Deformation Methods of Global Optimization in Chemistry and Physics
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    Chapter 14 Experimental Analysis of Algorithms
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    Chapter 15 Global Optimization: Software, Test Problems, and Applications
Attention for Chapter 9: Genetic Algorithms
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Chapter title
Genetic Algorithms
Chapter number 9
Book title
Handbook of Global Optimization
Published by
Springer, Boston, MA, January 2002
DOI 10.1007/978-1-4757-5362-2_9
Book ISBNs
978-1-4419-5221-9, 978-1-4757-5362-2
Authors

James E. Smith

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