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Evolutionary Algorithms, Swarm Dynamics and Complex Networks

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Cover of 'Evolutionary Algorithms, Swarm Dynamics and Complex Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Swarm and Evolutionary Dynamics as a Network
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    Chapter 2 Evolutionary Dynamics and Its Network Visualization - Selected Examples
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    Chapter 3 Differential Evolution Dynamics Modeled by Social Networks
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    Chapter 4 Conversion of SOMA Algorithm into Complex Networks
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    Chapter 5 Analysis of SOMA Algorithm Using Complex Network
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    Chapter 6 Improvement of SOMA Algorithm Using Complex Networks
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    Chapter 7 Complex Networks in Particle Swarm
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    Chapter 8 Comparison of Vertex Centrality Measures in Complex Network Analysis Based on Adaptive Artificial Bee Colony Algorithm
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    Chapter 9 Randomization and Complex Networks for Meta-Heuristic Algorithms
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    Chapter 10 Gallery of Evolutionary Networks
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    Chapter 11 Swarm Virus, Evolution, Behavior and Networking
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    Chapter 12 Simple Networks on Complex Cellular Automata: From de Bruijn Diagrams to Jump-Graphs
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    Chapter 13 A Hybrid Multi-objective Evolutionary Approach for Power Grid Topology Design
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    Chapter 14 Dynamic Analysis of Genetic Regulatory Networks with Delays
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    Chapter 15 Frontiers
Attention for Chapter 4: Conversion of SOMA Algorithm into Complex Networks
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Citations

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Chapter title
Conversion of SOMA Algorithm into Complex Networks
Chapter number 4
Book title
Evolutionary Algorithms, Swarm Dynamics and Complex Networks
Published by
Springer, Berlin, Heidelberg, January 2018
DOI 10.1007/978-3-662-55663-4_4
Book ISBNs
978-3-66-255661-0, 978-3-66-255663-4
Authors

Lukáš Tomaszek, Ivan Zelinka

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Computer Science 1 100%

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