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Nature-Inspired Algorithms for Optimisation

Overview of attention for book
Nature-Inspired Algorithms for Optimisation
Springer, Berlin, Heidelberg

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
Attention for Chapter 13: Algorithms Inspired in Social Phenomena
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Chapter title
Algorithms Inspired in Social Phenomena
Chapter number 13
Book title
Nature-Inspired Algorithms for Optimisation
Published in
Studies in Computational Intelligence, January 2009
DOI 10.1007/978-3-642-00267-0_13
Book ISBNs
978-3-64-200266-3, 978-3-64-200267-0
Authors

Antonio Neme, Sergio Hernández

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Librarian 1 17%
Lecturer 1 17%
Student > Bachelor 1 17%
Student > Ph. D. Student 1 17%
Professor > Associate Professor 1 17%
Other 0 0%
Unknown 1 17%
Readers by discipline Count As %
Computer Science 2 33%
Agricultural and Biological Sciences 1 17%
Engineering 1 17%
Design 1 17%
Unknown 1 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 July 2012.
All research outputs
#17,732,227
of 25,992,468 outputs
Outputs from Studies in Computational Intelligence
#1
of 1 outputs
Outputs of similar age
#159,454
of 187,124 outputs
Outputs of similar age from Studies in Computational Intelligence
#1
of 1 outputs
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So far Altmetric has tracked 1 research outputs from this source. They receive a mean Attention Score of 1.0. This one scored the same or higher as 0 of them.
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We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them