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Record Nr. |
UNINA9910438048503321 |
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Titolo |
Agent-based evolutionary search / / Ruhul Amin Sarker and Tapabrata Ray (Eds.) |
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Pubbl/distr/stampa |
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Berlin ; ; Heidelberg, : Springer-Verlag, c2010 |
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ISBN |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (X, 206 p.) |
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Collana |
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Adaptation, learning and optimization ; ; v. 5 |
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Altri autori (Persone) |
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SarkerRuhul A |
RayTapabrata |
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Disciplina |
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Soggetti |
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Multiagent systems |
Evolutionary computation |
Computer algorithms |
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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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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Machine Learning and Multiagent Systems as Interrelated Technologies -- Ant Colony Optimization for the Multi-criteria Vehicle Navigation Problem -- Solving Instances of the Capacitated Vehicle Routing Problem Using Multi-Agent Non-Distributed and Distributed Environment -- Structure vs. Efficiency of the Cross-Entropy Based Population Learning Algorithm for Discrete-Continuous Scheduling with Continuous Resource Discretisation -- Triple-Action Agents Solving the MRCPSP/max Problem -- Team of A-Teams - a Study of the Cooperation Between Program Agents Solving Difficult Optimization Problems -- Distributed Bregman-Distance Algorithms for Min-Max Optimization -- A Probability Collectives Approach for Multi-Agent Distributed and Cooperative Optimization with Tolerance for Agent Failure. |
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Sommario/riassunto |
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This volume presents a collection of original research works by leading specialists focusing on novel and promising approaches in which the multi-agent system paradigm is used to support, enhance or replace traditional approaches to solving difficult optimization problems. The editors have invited several well-known specialists to present their solutions, tools, and models falling under the common denominator of |
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