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[null,"1.","Record Nr.",null,"UNINA9910438048503321"]
[null,null,"Titolo",null,"Agent-based evolutionary search / / Ruhul Amin Sarker and Tapabrata Ray (Eds.)"]
[null,null,"Pubbl/distr/stampa",null,"Berlin ; ; Heidelberg, : Springer-Verlag, c2010"]
[null,null,"ISBN",null,"9783642340970"]
[{"$nulls":4},"3642340970"]
[null,null,"Edizione",null,"[1st ed. 2013.]"]
[null,null,"Descrizione fisica",null,"1 online resource (X, 206 p.)"]
[null,null,"Collana",null,"Adaptation, learning and optimization ; ; v. 5"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Multiagent systems"]
[{"$nulls":4},"Evolutionary computation"]
[{"$nulls":4},"Computer algorithms"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Machine Learning and Multiagent Systems as Interrelated Technologies -- Ant Colony Optimization for the Multi-criteria Vehicle Navigation Problem -- Solving Instances of the Capacitated Vehicle Routing Problem Using Multi-Agent Non-Distributed and Distributed Environment -- Structure vs. Efficiency of the Cross-Entropy Based Population Learning Algorithm for Discrete-Continuous Scheduling with Continuous Resource Discretisation -- Triple-Action Agents Solving the MRCPSP/max Problem -- Team of A-Teams - a Study of the Cooperation Between Program Agents Solving Difficult Optimization Problems -- Distributed Bregman-Distance Algorithms for Min-Max Optimization -- A Probability Collectives Approach for Multi-Agent Distributed and Cooperative Optimization with Tolerance for Agent Failure."]
[null,null,null,"Sommario/riassunto","This volume presents a collection of original research works by leading specialists focusing on novel and promising approaches in which the multi-agent system paradigm is used to support, enhance or replace traditional approaches to solving difficult optimization problems. The editors have invited several well-known specialists to present their solutions, tools, and models falling under the common denominator of the agent-based optimization. The book consists of eight chapters covering examples of application of the multi-agent paradigm and respective customized tools to solve  difficult optimization problems arising in different areas such as machine learning, scheduling, "]
[{"$nulls":4},"transportation and, more generally, distributed and cooperative problem solving."]