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Nature-Inspired Algorithms for Optimisation

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Table of Contents

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    Book Overview
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    Chapter 1 Why Is Optimization Difficult?
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    Chapter 2 The Rationale Behind Seeking Inspiration from Nature
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    Chapter 3 The Evolutionary-Gradient-Search Procedure in Theory and Practice
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    Chapter 4 The Evolutionary Transition Algorithm: Evolving Complex Solutions Out of Simpler Ones
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    Chapter 5 A Model-Assisted Memetic Algorithm for Expensive Optimization Problems
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    Chapter 6 A Self-adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large Scale Global Optimization
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    Chapter 7 Differential Evolution with Fitness Diversity Self-adaptation
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    Chapter 8 Central Pattern Generators: Optimisation and Application
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    Chapter 9 Fish School Search
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    Chapter 10 Magnifier Particle Swarm Optimization
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    Chapter 11 Improved Particle Swarm Optimization in Constrained Numerical Search Spaces
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    Chapter 12 Applying River Formation Dynamics to Solve NP-Complete Problems
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    Chapter 13 Algorithms Inspired in Social Phenomena
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    Chapter 14 Artificial Immune Systems for Optimization
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    Chapter 15 Ranking Methods in Many-Objective Evolutionary Algorithms
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    Chapter 16 On the Effect of Applying a Steady-State Selection Scheme in the Multi-Objective Genetic Algorithm NSGA-II
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    Chapter 17 Improving the Performance of Multiobjective Evolutionary Optimization Algorithms Using Coevolutionary Learning
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    Chapter 18 Evolutionary Optimization for Multiobjective Portfolio Selection under Markowitz’s Model with Application to the Caracas Stock Exchange
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Title
Nature-Inspired Algorithms for Optimisation
Published by
Springer, Berlin, Heidelberg, January 2009
DOI 10.1007/978-3-642-00267-0
ISBNs
978-3-64-200266-3, 978-3-64-200267-0
Editors

Raymond Chiong

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 1%
India 1 1%
Unknown 84 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 23 27%
Student > Ph. D. Student 21 24%
Researcher 9 10%
Student > Postgraduate 5 6%
Student > Bachelor 4 5%
Other 13 15%
Unknown 11 13%
Readers by discipline Count As %
Engineering 32 37%
Computer Science 24 28%
Physics and Astronomy 3 3%
Mathematics 2 2%
Earth and Planetary Sciences 2 2%
Other 7 8%
Unknown 16 19%