Evolutionary Algorithms, Swarm Dynamics and Complex Networks: Methodology, Perspectives and Implementation

Description

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. Front Matter ....Pages i-xxii Front Matter ....Pages 1-1 Swarm and Evolutionary Dynamics as a Network (Ivan Zelinka)....Pages 3-29 Evolutionary Dynamics and Its Network Visualization - Selected Examples (Orkhan Yarakhmedov, Victor Polyakh, Ivan Chernogorov, Ivan Zelinka)....Pages 31-63 Front Matter ....Pages 65-65 Differential Evolution Dynamics Modeled by Social Networks (Lenka Skanderová, Ivan Zelinka)....Pages 67-100 Conversion of SOMA Algorithm into Complex Networks (Lukáš Tomaszek, Ivan Zelinka)....Pages 101-114 Analysis of SOMA Algorithm Using Complex Network (Lukáš Tomaszek, Ivan Zelinka)....Pages 115-129 Improvement of SOMA Algorithm Using Complex Networks (Lukáš Tomaszek, Ivan Zelinka)....Pages 131-143 Complex Networks in Particle Swarm (Michal Pluhacek, Roman Šenkeřík, Adam Viktorin, Tomas Kadavy)....Pages 145-159 Comparison of Vertex Centrality Measures in Complex Network Analysis Based on Adaptive Artificial Bee Colony Algorithm (Magdalena Metlicka, Donald Davendra)....Pages 161-176 Randomization and Complex Networks for Meta-Heuristic Algorithms (Roman Šenkeřík, Ivan Zelinka, Michal Pluhacek, Adam Viktorin, Jakub Janostik, Zuzana Kominkova Oplatkova)....Pages 177-194 Gallery of Evolutionary Networks (Ivan Zelinka, Roman Šenkeřík, Michal Pluháček)....Pages 195-210 Front Matter ....Pages 211-211 Swarm Virus, Evolution, Behavior and Networking (Lubomir Sikora, Ivan Zelinka)....Pages 213-239 Simple Networks on Complex Cellular Automata: From de Bruijn Diagrams to Jump-Graphs (Genaro J. Martínez, Andrew Adamatzky, Bo Chen, Fangyue Chen, Juan C. Seck-Tuoh-Mora)....Pages 241-264 A Hybrid Multi-objective Evolutionary Approach for Power Grid Topology Design (Xiaowen Bi, Wallace K. S. Tang)....Pages 265-284 Dynamic Analysis of Genetic Regulatory Networks with Delays (Zhi-Hong Guan, Guang Ling)....Pages 285-309 Frontiers (Ivan Zelinka)....Pages 311-312