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Record Nr. |
UNISA996465488603316 |
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Titolo |
Swarm Intelligence Based Optimization [[electronic resource] ] : Second International Conference, ICSIBO 2016, Mulhouse, France, June 13-14, 2016, Revised Selected Papers / / edited by Patrick Siarry, Lhassane Idoumghar, Julien Lepagnot |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (IX, 125 p. 56 illus.) |
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Collana |
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Theoretical Computer Science and General Issues, , 2512-2029 ; ; 10103 |
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Disciplina |
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Soggetti |
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Algorithms |
Artificial intelligence |
Computer systems |
Computers, Special purpose |
Microprocessors |
Computer architecture |
Computer science—Mathematics |
Discrete mathematics |
Artificial Intelligence |
Computer System Implementation |
Special Purpose and Application-Based Systems |
Processor Architectures |
Discrete Mathematics in Computer Science |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Theoretical advances of swarm intelligence metaheuristics -- Combinatorial discrete, binary, constrained, multi-objective, multi-modal, dynamic, noisy, and large scale optimization -- Artificial immune systems, particle swarms, ant colony, bacterial forging, artificial bees, fireflies algorithm -- Hybridization of algorithms -- Parallel/distributed computing, machine learning, data mining, data |
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clustering, decision making and multi-agent systems based on swarm intelligence principles -- Adaptation and applications of swarm intelligence principles to real world problems in various domains. |
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Sommario/riassunto |
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This book constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on Swarm Intelligence Based Optimization, ICSIBO 2016, held in Mulhouse, France, in June 2016. The 9 full papers presented were carefully reviewed and selected from 20 submissions. They are centered around the following topics: theoretical advances of swarm intelligence metaheuristics; combinatorial discrete, binary, constrained, multi-objective, multi-modal, dynamic, noisy, and large scale optimization; artificial immune systems, particle swarms, ant colony, bacterial forging, artificial bees, fireflies algorithm; hybridization of algorithms; parallel/distributed computing, machine learning, data mining, data clustering, decision making and multi-agent systems based on swarm intelligence principles; adaptation and applications of swarm intelligence principles to real world problems in various domains. |
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