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EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V [electronic resource] / edited by Alexandru-Adrian Tantar, Emilia Tantar, Jian-Qiao Sun, Wei Zhang, Qian Ding, Oliver Sch�utze, Michael Emmerich, Pierrick Legrand, Pierre Del Moral, Carlos A. Coello Coello.

Contributor(s): Tantar, Alexandru-Adrian [editor.] | Tantar, Emilia [editor.] | Sun, Jian-Qiao [editor.] | Zhang, Wei [editor.] | Ding, Qian [editor.] | Sch�utze, Oliver [editor.] | Emmerich, Michael [editor.] | Legrand, Pierrick [editor.] | Del Moral, Pierre [editor.] | Coello Coello, Carlos A [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Advances in Intelligent Systems and Computing: 288Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XIV, 336 p. 106 illus., 37 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319074948.Subject(s): Engineering | Artificial intelligence | Computational intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Set Oriented Numerics -- Computational Game Theory -- Machine Learning Applied to Networks -- Complex Networks and Landscape Analysis -- Local Search and Optimization -- Genetic Programming -- Evolutionary Multiobjective Optimization -- Practical Aspects of Evolutionary Algorithms.
In: Springer eBooksSummary: This volume encloses research articles that were presented at the EVOLVE 2014 International Conference in Beijing, China, July 1-4, 2014.The book gathers contributions that emerged from the conference tracks, ranging from probability to set oriented numerics and evolutionary computation; all complemented by the bridging purpose of the conference, e.g. Complex Networks and Landscape Analysis, or by the more application oriented perspective. The novelty of the volume, when considering the EVOLVE series, comes from targeting also the practitioner's view. This is supported by the Machine Learning Applied to Networks and Practical Aspects of Evolutionary Algorithms tracks, providing surveys on new application areas, as in the networking area and useful insights in the development of evolutionary techniques, from a practitioner's perspective. Complementary to these directions, the conference tracks supporting the volume, follow on the individual advancements of the subareas constituting the scope of the conference, through the Computational Game Theory, Local Search and Optimization, Genetic Programming, Evolutionary Multi-objective optimization tracks.
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Set Oriented Numerics -- Computational Game Theory -- Machine Learning Applied to Networks -- Complex Networks and Landscape Analysis -- Local Search and Optimization -- Genetic Programming -- Evolutionary Multiobjective Optimization -- Practical Aspects of Evolutionary Algorithms.

This volume encloses research articles that were presented at the EVOLVE 2014 International Conference in Beijing, China, July 1-4, 2014.The book gathers contributions that emerged from the conference tracks, ranging from probability to set oriented numerics and evolutionary computation; all complemented by the bridging purpose of the conference, e.g. Complex Networks and Landscape Analysis, or by the more application oriented perspective. The novelty of the volume, when considering the EVOLVE series, comes from targeting also the practitioner's view. This is supported by the Machine Learning Applied to Networks and Practical Aspects of Evolutionary Algorithms tracks, providing surveys on new application areas, as in the networking area and useful insights in the development of evolutionary techniques, from a practitioner's perspective. Complementary to these directions, the conference tracks supporting the volume, follow on the individual advancements of the subareas constituting the scope of the conference, through the Computational Game Theory, Local Search and Optimization, Genetic Programming, Evolutionary Multi-objective optimization tracks.

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