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2018 | Book

Swarm and Evolutionary Dynamics as a Network Read chapter Read first chapter

Evolutionary Algorithms, Swarm Dynamics and Complex Networks

Methodology, Perspectives and Implementation

Editors: Ivan Zelinka, Prof. Guanrong Chen

Publisher: Springer Berlin Heidelberg

Book Series : Emergence, Complexity and Computation

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

Evolutionary algorithms constitute a class of well-known algorithms, which are designed based on the Darwinian theory of evolution and Mendelian theory of heritage. They are partly based on random and partly based on deterministic principles. Due to this nature, it is challenging to predict and control its performance in solving complex nonlinear problems. Recently, the study of evolutionary dynamics is focused not only on the traditional investigations but also on the understanding and analyzing new principles, with the intention of controlling and utilizing their properties and performances toward more effective real-world applications. In this book, based on many years of intensive research of the authors, is proposing novel ideas about advancing evolutionary dynamics towards new phenomena including many new topics, even the dynamics of equivalent social networks. In fact, it includes more advanced complex networks and incorporates them with the CMLs (coupled map lattices), which are usually used for spatiotemporal complex systems simulation and analysis, based on the observation that chaos in CML can be controlled, so does evolution dynamics. All the chapter authors are, to the best of our knowledge, originators of the ideas mentioned above and researchers on evolutionary algorithms and chaotic dynamics as well as complex networks, who will provide benefits to the readers regarding modern scientific research on related subjects.

Table of Contents

Frontmatter

Theory

Frontmatter
Chapter 1. Swarm and Evolutionary Dynamics as a Network
Abstract
This chapter is an introduction to a novel method for visualizing the dynamics of evolutionary algorithms in the form of networks. The whole idea is based on the obvious similarity between interactions between individuals in a swarm and evolutionary algorithms and for example, users of social networks, linking between web pages, etc. The analogy between individuals in populations in an arbitrary evolutionary algorithm and vertices of a network is discussed, as well as between edges in a network and communication between individuals in a population. The possibility of visualizing the dynamics of network using the coupled map lattices method and control using chaos control techniques are also discussed. The chapter is introductory, for more details it is recommended to read referenced sources.
Ivan Zelinka
Chapter 2. Evolutionary Dynamics and Its Network Visualization - Selected Examples
Abstract
In this chapter is demonstrated, on selected algorithms, how can be converted dynamics of the evolutionary process into a network. Selected algorithms are a self-organizing migrating algorithm, differential evolution, particle swarm, artificial bee colony and ant colony optimization. The main ideas and steps are discussed here, for more detailed study and understanding references to original research papers are throughout the text. The aim of this chapter is to show principles of conversion with attention on the fact that there is no universal guide on how to do conversion - it is a matter of creative approach and algorithm knowledge.
Orkhan Yarakhmedov, Victor Polyakh, Ivan Chernogorov, Ivan Zelinka

Applications

Frontmatter
Chapter 3. Differential Evolution Dynamics Modeled by Social Networks
Abstract
During the last years, social networks have become a normal part of our lives. Some people can not imagine the world without the social networks yet. They are considered to be an appropriate tool for communication, advertisement, or even business. Beside the indisputable importance of the social networks for the users, they bring very valuable information for researchers from whole of the world. The social network analysis is used to better understand some principles of difficult systems. In this chapter, they are used to model and better understand the relationships between individuals in the differential evolution algorithm. The short-interval networks, aggregated networks, and longitudinal social networks will be taken into consideration and the results of the different analysis will be discussed.
Lenka Skanderová, Ivan Zelinka
Chapter 4. Conversion of SOMA Algorithm into Complex Networks
Abstract
In this chapter we describe possibility how to create complex network from SOMA algorithm and we describe what this network represents according to algorithm. Also we look at the visualization of such networks and show basic global complex network properties and how they look during the time.
Lukáš Tomaszek, Ivan Zelinka
Chapter 5. Analysis of SOMA Algorithm Using Complex Network
Abstract
In this chapter, that is a continuation of the previous one; we show some basic complex network analysis of SOMA algorithm on selected cost functions. The interesting facts, which can be used to improve the algorithm, are discussed here. Also, comparison of different runs of SOMA algorithm on the same test functions is presented here.
Lukáš Tomaszek, Ivan Zelinka
Chapter 6. Improvement of SOMA Algorithm Using Complex Networks
Abstract
In this chapter, we focus on improvement of SOMA algorithm based on the previous analysis. We show some possibilities how to improve SOMA algorithm according to complex network analysis. At the end of the chapter, we show the best possible option how to improve standard SOMA algorithm together with results of a statistical test. Proposed improvements can be made (in principle) on arbitrary algorithm, SOMA here is used only for demonstrative purposes.
Lukáš Tomaszek, Ivan Zelinka
Chapter 7. Complex Networks in Particle Swarm
Abstract
This chapter presents an proposal of methodology for converting the inner dynamics of PSO algorithm into complex network. The motivation is in the recent trend of adaptive and learning methods for improving the performance of evolutionary computational techniques. It seems very likely that the complex network and its statistical characteristics can be used within those adaptive approaches. The network analysis also provides usefull insight into the inner dynamic of PSO. The methodology described in this chapter uses the communication in the swarm for construction of the network.
Michal Pluhacek, Roman Šenkeřík, Adam Viktorin, Tomas Kadavy
Chapter 8. Comparison of Vertex Centrality Measures in Complex Network Analysis Based on Adaptive Artificial Bee Colony Algorithm
Abstract
Evolutionary algorithms are a powerful tool for difficult optimization problems infeasible for conventional approach. Unfortunately, many of them are not free of problems of premature convergence and stagnation. The algorithm design constantly strives for improved performance. Next to the efforts of developing EAs based on entirely new principles, the existing EAs are being improved with advanced techniques, which seek to remedy the afore mentioned problems in existing algorithms, either employing the parameter adaptation techniques, or utilizing the information obtained from overall population analysis to control its development. As discovered earlier, the population dynamics of many evolutionary algorithms exhibit complex network properties. The analysis of such network can be used to obtain the meaningful information about population development in time. Based on these ideas, an adaptive mechanism was implemented inside swarm based ABC algorithm, employing complex network analysis, vertex centralities of degree, closeness and betweenness in particular, to enhance algorithm’s performance. However, since both versions of algorithm were originally using all three of the chosen centralities in combination, the impact of individual centralities on algorithm performance was not clear. This chapter provides insight into this, by comparing and analysing the results of Adaptive ABC algorithm using single selected centrality.
Magdalena Metlicka, Donald Davendra
Chapter 9. Randomization and Complex Networks for Meta-Heuristic Algorithms
Abstract
This chapter deals with the hybridization of the chaos driven heuristics concept and complex networks framework for meta-heuristic. This research aims on the experimental investigations on the time development and influence of different randomization types, different strategies for Differential Evolution (DE) through the analysis of complex network as a record of population dynamics and indices selection. The population is visualized as an evolving complex network, which exhibits non-trivial features such as adjacency graph, centralities, clustering coefficient and other attributes showing efficiency of the network. Experiments were performed for different DE strategies, several different randomization types and simple test functions.
Roman Šenkeřík, Ivan Zelinka, Michal Pluhacek, Adam Viktorin, Jakub Janostik, Zuzana Kominkova Oplatkova
Chapter 10. Gallery of Evolutionary Networks
Abstract
This chapter is a graphical overview - a gallery of selected networks that have been obtained during our experiments. The gallery contains samples coming from different algorithms with attention on its beauty (as we hope) and shall serve as the visual motivation-inspiration for new experiments. Visualization of those networks has been done on different principles with only one aim: to show a variety of visualizations. Beside standard visualization, are present also visualizations like community, degree centralities, etc.
Ivan Zelinka, Roman Šenkeřík, Michal Pluháček

Miscellanies

Frontmatter
Chapter 11. Swarm Virus, Evolution, Behavior and Networking
Abstract
In this chapter we would like to outline how behavior of malicious software, i.e. computer virus can be connected with evolution and visualization of its spreading as the network. The approach presented here is not based on single classical virus spreading, but more on hypothetical swarm virus and its dynamics of spread in PC. The latest development of virus code shows, that CnC technology (command and control) has been used as in the case of Stuxnet virus or Botnet malware. It is logical to expect that development of the viral code will never stop at this level, but will continue up to viruses that will evolve according to the Darwinian theory of evolution and will mimic swarm in the nature, such as the swarm algorithms already do. The aim of our research is not developing a swarm virus, but using its expectable behavior we show that its dynamics can be then modeled as the network structure and thus likely controlled and stopped, as our experiments in the first part of this book suggest. The same methodology can be used not only in laboratory conditions on a single PC, but also on virus spreading over the network or Internet, if real data is available. Ideas and results of this chapter were also presented on Post Graduation in Cyber Security and Ciberdefense, Multinational Cyber Defence Education and Training Project, NATO Smart Defence Project, Lisbon 2017.
Lubomir Sikora, Ivan Zelinka
Chapter 12. Simple Networks on Complex Cellular Automata: From de Bruijn Diagrams to Jump-Graphs
Abstract
We overview networks which characterise dynamics in cellular automata. These networks are derived from one-dimensional cellular automaton rules and global states of the automaton evolution: de Bruijn diagrams, subsystem diagrams, basins of attraction, and jump-graphs. These networks are used to understand properties of spatially-extended dynamical systems: emergence of non-trivial patterns, self-organisation, reversibility and chaos. Particular attention is paid to networks determined by travelling self-localisations, or gliders.
Genaro J. Martínez, Andrew Adamatzky, Bo Chen, Fangyue Chen, Juan C. Seck-Tuoh-Mora
Chapter 13. A Hybrid Multi-objective Evolutionary Approach for Power Grid Topology Design
Abstract
Power grid is one of the critical infrastructures in human society. It is highly complex in both structure and dynamics. In order to study its performance, different models, such as Kuramoto oscillator network model, power flow model, cascading load model and so on, have been suggested. In this chapter, it is to demonstrate how an evolutionary algorithm can be applied to effectively solve the topological design problem in power grid based on the Kuramoto oscillator network model. Recognizing that multiple criteria are commonly confronted in practice, a multi-objective evolutionary algorithm is developed. Two objectives, namely the network synchronizability and the cost, are considered in this work. In addition, since the design problem is complex and nonlinear, a dedicated local searching mechanism is embedded to enhance the searching capability of the algorithm. Finally, the effectiveness of the proposed algorithm is confirmed by extensive numerical simulations.
Xiaowen Bi, Wallace K. S. Tang
Chapter 14. Dynamic Analysis of Genetic Regulatory Networks with Delays
Abstract
Many biological systems have the conspicuous property to present more than one stable state and diverse rhythmic behaviors. A closed relationship has been witnessed by the pioneering works between these complex dynamic behaviors and cyclic genetic structures. This chapter analyzes the stability and bifurcation criteria of cyclic genetic regulatory networks with time delays. Not only the single cyclic genetic regulatory network but also a typical coupled cyclic genetic regulatory network through direct communication mechanism are introduced to enlighten further the dynamical evolution of living things.
Zhi-Hong Guan, Guang Ling
Chapter 15. Frontiers
Abstract
This book is dedicated to unconventional view on the swarm and evolutionary algorithm dynamics. The main aim was to introduce the main ideas, the most important steps and report selected experiments we have done on that field. The book does not present all possible visualizations, conversions, and experiments on control of algorithm dynamics via approach proposed here. Despite the fact that almost all ideas presented here were in the detailed form published on various conferences journals and also books as the book chapter, still, a lot of open questions is there. Let’s discuss a bit those topics now, as our opinion and possible inspiration for further research.
Ivan Zelinka
Metadata
Title
Evolutionary Algorithms, Swarm Dynamics and Complex Networks
Editors
Ivan Zelinka
Prof. Guanrong Chen
Copyright Year
2018
Publisher
Springer Berlin Heidelberg
Electronic ISBN
978-3-662-55663-4
Print ISBN
978-3-662-55661-0
DOI
https://doi.org/10.1007/978-3-662-55663-4

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