03400nam 2200613Ia 450 991043804850332120200520144314.09783642340970364234097010.1007/978-3-642-34097-0(CKB)3400000000102793(SSID)ssj0000878429(PQKBManifestationID)11483213(PQKBTitleCode)TC0000878429(PQKBWorkID)10836041(PQKB)10683082(DE-He213)978-3-642-34097-0(MiAaPQ)EBC3071001(PPN)168326132(EXLCZ)99340000000010279320100909d2010 uy 0engurnn#008mamaatxtccrAgent-based evolutionary search /Ruhul Amin Sarker and Tapabrata Ray (Eds.)1st ed. 2013.Berlin ;Heidelberg Springer-Verlagc20101 online resource (X, 206 p.)Adaptation, learning and optimization ;v. 5Bibliographic Level Mode of Issuance: Monograph9783642340963 3642340962 Includes bibliographical references and index.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.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, transportation and, more generally, distributed and cooperative problem solving.Studies in Computational Intelligence,1860-949X ;456Multiagent systemsEvolutionary computationComputer algorithmsMultiagent systems.Evolutionary computation.Computer algorithms.006.3Sarker Ruhul A1369423Ray Tapabrata1763711MiAaPQMiAaPQMiAaPQBOOK9910438048503321Agent-based evolutionary search4204307UNINA