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Record Nr. |
UNINA9910349432903321 |
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Titolo |
Advances in Swarm Intelligence : 9th International Conference, ICSI 2018, Shanghai, China, June 17-22, 2018, Proceedings, Part I / / edited by Ying Tan, Yuhui Shi, Qirong Tang |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (XXIV, 639 p. 183 illus.) |
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Collana |
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Theoretical Computer Science and General Issues, , 2512-2029 ; ; 10941 |
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Disciplina |
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Soggetti |
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Algorithms |
Artificial intelligence |
Computer networks |
Computers, Special purpose |
Software engineering |
Computer science |
Artificial Intelligence |
Computer Communication Networks |
Special Purpose and Application-Based Systems |
Software Engineering |
Models of Computation |
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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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Theories and models of swarm intelligence -- ant colony optimization; particle swarm optimization -- artificial bee colony algorithms -- genetic algorithms -- differential evolution -- fireworks algorithms -- bacterial foraging optimization -- artificial immune system -- hydrologic cycle optimization -- other swarm-based optimization algorithms -- hybrid optimization algorithms -- multi-objective optimization -- large-scale global optimization -- multi-agent systems -- swarm robotics; fuzzy logic approaches -- planning and routing problems -- recommendation in social media -- prediction -- classification -- finding patterns -- image enhancement -- deep |
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Sommario/riassunto |
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The two-volume set of LNCS 10941 and 10942 constitutes the proceedings of the 9th International Conference on Advances in Swarm Intelligence, ICSI 2018, held in Shanghai, China, in June 2018. The total of 113 papers presented in these volumes was carefully reviewed and selected from 197 submissions. The papers were organized in topical sections as follows: theories and models of swarm intelligence; ant colony optimization; particle swarm optimization; artificial bee colony algorithms; genetic algorithms; differential evolution; fireworks algorithms; bacterial foraging optimization; artificial immune system; hydrologic cycle optimization; other swarm-based optimization algorithms; hybrid optimization algorithms; multi-objective optimization; large-scale global optimization; multi-agent systems; swarm robotics; fuzzy logic approaches; planning and routing problems; recommendation in social media; prediction, classification; finding patterns; image enhancement; deep learning. . |
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