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Hybrid Evolutionary Algorithms

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Cover of 'Hybrid Evolutionary Algorithms'

Table of Contents

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    Book Overview
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    Chapter 1 Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews
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    Chapter 2 Quantum-Inspired Evolutionary Algorithm for Numerical Optimization
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    Chapter 3 Enhanced Evolutionary Algorithms for Multidisciplinary Design Optimization: A Control Engineering Perspective
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    Chapter 4 Hybrid Evolutionary Algorithms and Clustering Search
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    Chapter 5 A Novel Hybrid Algorithm for Function Optimization: Particle Swarm Assisted Incremental Evolution Strategy
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    Chapter 6 An Efficient Nearest Neighbor Classifier
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    Chapter 7 Hybrid Genetic: Particle Swarm Optimization Algorithm
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    Chapter 8 A Hybrid Genetic Algorithm and Bacterial Foraging Approach for Global Optimization and Robust Tuning of PID Controller with Disturbance Rejection
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    Chapter 9 Memetic Algorithms Parametric Optimization for Microlithography
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    Chapter 10 Significance of Hybrid Evolutionary Computation for Ab Initio Protein Folding Prediction
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    Chapter 11 A Hybrid Evolutionary Heuristic for Job Scheduling on Computational Grids
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    Chapter 12 Clustering Gene-Expression Data: A Hybrid Approach that Iterates Between k-Means and Evolutionary Search
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    Chapter 13 Robust Parametric Image Registration
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    Chapter 14 Pareto Evolutionary Algorithm Hybridized with Local Search for Biobjective TSP
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Title
Hybrid Evolutionary Algorithms
Published by
Springer-Verlag Berlin Heidelberg, January 2007
DOI 10.1007/978-3-540-73297-6
ISBNs
978-3-54-073296-9, 978-3-54-073297-6, 978-3-64-209235-0
Editors

Crina Grosan, Ajith Abraham, Hisao Ishibuchi

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Portugal 1 3%
Poland 1 3%
France 1 3%
Unknown 36 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 35%
Student > Bachelor 4 10%
Student > Master 4 10%
Researcher 3 8%
Student > Doctoral Student 2 5%
Other 6 15%
Unknown 7 18%
Readers by discipline Count As %
Computer Science 17 43%
Engineering 9 23%
Agricultural and Biological Sciences 3 8%
Mathematics 3 8%
Unknown 8 20%