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[null,"1.","Record Nr.",null,"UNINA9910299491003321"]
[null,null,"Autore",null,"Kramer Oliver"]
[null,null,"Titolo",null,"A brief introduction to continuous evolutionary optimization / / Oliver Kramer"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , 2014"]
[null,null,"ISBN",null,"3-319-03422-7"]
[null,null,"Edizione",null,"[1st ed. 2014.]"]
[null,null,"Descrizione fisica",null,"1 online resource (xi, 94 pages) : illustrations (some color)"]
[null,null,"Collana",null,"SpringerBriefs in Computational Intelligence, , 2625-3704"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Evolutionary computation"]
[{"$nulls":4},"Computational intelligence"]
[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","\"ISSN: 2191-530X.\""]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Part I Foundations -- Part II Advanced Optimization -- Part III Learning -- Part IV Appendix."]
[null,null,null,"Sommario/riassunto","Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal, and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel parameters of the Nadaraya-Watson estimator, and a swarm-based iterative approach is presented for optimizing latent points in dimensionality reduction problems. Experiments on typical benchmark problems as well as numerous figures and diagrams illustrate the behavior of the introduced concepts and methods."]
[null,"2.","Record Nr.",null,"UNINA9910254170503321"]
[null,null,"Autore",null,"Kramer Oliver"]
[null,null,"Titolo",null,"Genetic Algorithm Essentials / / by Oliver Kramer"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017"]
[null,null,"ISBN",null,"3-319-52156-X"]
[null,null,"Edizione",null,"[1st ed. 2017.]"]
[null,null,"Descrizione fisica",null,"1 online resource (IX, 92 p. 38 illus. in color.)"]
[null,null,"Collana",null,"Studies in Computational Intelligence, , 1860-949X ; ; 679"]
[null,null,"Disciplina",null,"519.7"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Artificial Intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Part I: Foundations -- Introduction -- Genetic Algorithms -- Parameters -- Part II: Solution Spaces -- Multimodality -- Constraints -- Multiple Objectives -- Part III: Advanced Concepts -- Theory -- Machine Learning -- Applications -- Part IV: Ending -- Summary and Outlook -- Index -- References."]
[null,null,null,"Sommario/riassunto","This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications."]