00966nam0-22003131i-450-99000456790040332120150528084144.0000456790FED01000456790(Aleph)000456790FED0100045679019990604d1955----km-y0itay50------bagery-------001yyCelio Secondo Curionesein Leben und sein Werke (1503-1569)Markus KutterBasel [etc.]Helbing e Lichtenhahn1955310 p.24 cmBasler Beitrage zur Geschichtswissenschaft54Curio, Coelius SecundusCurione, Celio Secondo (Umanista protestante, Ciriè 1503 - Basilea 1569)UmanesimoSec. 16.273Kutter,Markus180636ITUNINARICAUNIMARCBK990004567900403321273.6 CUR 1Bibl. 29927FLFBCFLFBCUNINA02924oam 2200457 450 991029949100332120190911112726.03-319-03422-710.1007/978-3-319-03422-5(OCoLC)877106574(MiFhGG)GVRL6XBD(EXLCZ)99371000000007506120131105d2014 uy 0engurun|---uuuuatxtccrA brief introduction to continuous evolutionary optimization /Oliver Kramer1st ed. 2014.Cham, Switzerland :Springer,2014.1 online resource (xi, 94 pages) illustrations (some color)SpringerBriefs in Computational Intelligence,2625-3704"ISSN: 2191-530X."3-319-03421-9 Includes bibliographical references and index.Part I Foundations -- Part II Advanced Optimization -- Part III Learning -- Part IV Appendix.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.SpringerBriefs in applied sciences and technology.Computational intelligence.Evolutionary computationComputational intelligenceEvolutionary computation.Computational intelligence.006.3Kramer Oliverauthttp://id.loc.gov/vocabulary/relators/aut761919MiFhGGMiFhGGBOOK9910299491003321A Brief Introduction to Continuous Evolutionary Optimization1951234UNINA