@article{824877,
      recid = {824877},
      author = {Zelinka, Ivan, and Chen, G.},
      title = {Evolutionary algorithms, swarm dynamics and complex  networks : methodology, perspectives and implementation /},
      publisher = {Springer,},
      address = {Berlin, Germany :},
      pages = {1 online resource (322 pages)},
      year = {2018},
      abstract = {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. .},
      url = {http://library.usi.edu/record/824877},
      doi = {https://doi.org/10.1007/978-3-662-55663-4},
}