01399nam a2200361 i 4500991002501879707536cr nn 008mamaa140429s2013 de | s |||| 0|eng d9783642321603b14184679-39ule_instBibl. Dip.le Aggr. Matematica e Fisica - Sez. Matematicaeng515.35323AMS 35R02AMS 35L65AMS 90B20Ambrosio, Luigi44009Modelling and optimisation of flows on networks[e-book] :Cetraro, Italy 2009 /by Luigi Ambrosio ... [et al.] ; editors: Benedetto Piccoli, Michel RascleBerlin :Springer,20131 online resource (xiv, 497 p.)Lecture Notes in Mathematics,0075-8434 ;2062MathematicsGlobal analysis (Mathematics)Partial differential equationsMathematical modeling and industrial mathematicsPiccoli, BenedettoRascle, MichelSpringer eBookshttp://dx.doi.org/10.1007/978-3-642-32160-3An electronic book accessible through the World Wide Web.b1418467903-03-2224-04-14991002501879707536Modelling and optimisation of flows on networks258684UNISALENTOle01324-04-14m@ -engde 0003275nam 22006615 450 991073409460332120230719192512.03-319-07407-510.1007/978-3-319-07407-8(CKB)3710000000521514(EBL)4179463(SSID)ssj0001584291(PQKBManifestationID)16265715(PQKBTitleCode)TC0001584291(PQKBWorkID)14866306(PQKB)11309579(DE-He213)978-3-319-07407-8(MiAaPQ)EBC4179463(PPN)19053270X(EXLCZ)99371000000052151420151127d2015 u| 0engur|n|---|||||txtccrMultimodal Optimization by Means of Evolutionary Algorithms /by Mike Preuss1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (206 p.)Natural Computing Series,2627-6461Description based upon print version of record.3-319-07406-7 Includes bibliographical references.Introduction: Towards Multimodal Optimization -- Experimentation in Evolutionary Computation -- Groundwork for Niching -- Nearest-Better Clustering -- Niching Methods and Multimodal Optimization Performance -- Nearest-Better Based Niching.This book offers the first comprehensive taxonomy for multimodal optimization algorithms, work with its root in topics such as niching, parallel evolutionary algorithms, and global optimization. The author explains niching in evolutionary algorithms and its benefits; he examines their suitability for use as diagnostic tools for experimental analysis, especially for detecting problem (type) properties; and he measures and compares the performances of niching and canonical EAs using different benchmark test problem sets. His work consolidates the recent successes in this domain, presenting and explaining use cases, algorithms, and performance measures, with a focus throughout on the goals of the optimization processes and a deep understanding of the algorithms used. The book will be useful for researchers and practitioners in the area of computational intelligence, particularly those engaged with heuristic search, multimodal optimization, evolutionary computing, and experimental analysis.Natural Computing Series,2627-6461AlgorithmsComputational intelligenceMathematical optimizationAlgorithmsComputational IntelligenceOptimizationAlgorithms.Computational intelligence.Mathematical optimization.Algorithms.Computational Intelligence.Optimization.006.336Preuss Mikeauthttp://id.loc.gov/vocabulary/relators/aut1371380MiAaPQMiAaPQMiAaPQBOOK9910734094603321Multimodal Optimization by Means of Evolutionary Algorithms3400413UNINA