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Verlag: Springer Berlin Heidelberg 2010-10-19, Berlin, 2010
ISBN 10: 3642167497ISBN 13: 9783642167492
Anbieter: Blackwell's, London, Vereinigtes Königreich
Buch
paperback. Zustand: New. Language: ENG.
Verlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 2007
ISBN 10: 3540497722ISBN 13: 9783540497721
Anbieter: Ammareal, Morangis, Frankreich
Buch
Hardcover. Zustand: Très bon. Ancien livre de bibliothèque. Salissures sur la tranche. Traces d'humidité sur les premières et dernières pages. Edition 2007. Tome 51. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Stains on the edge. Traces of humidity on the first and last pages. Edition 2007. Volume 51. Ammareal gives back up to 15% of this item's net price to charity organizations.
Verlag: Springer Berlin Heidelberg, 2010
ISBN 10: 3642167497ISBN 13: 9783642167492
Anbieter: moluna, Greven, Deutschland
Buch
Kartoniert / Broschiert. Zustand: New. Session 1. Modeling and Simulation of Biological Processes.- A Formal Model for Gene Regulatory Networks with Time Delays.- Modelling fim Expression in Escherichia Coli K12.- Study of the Structural Pathology Caused by CYP2C9 Polymorphisms towards Flurbipro.
Verlag: Springer International Publishing, 2019
ISBN 10: 3030027287ISBN 13: 9783030027285
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book bridges the widening gap between two crucial constituents of computational intelligence: the rapidly advancing technologies of machine learning in the digital information age, and the relatively slow-moving field of general-purpose search and optimization algorithms. With this in mind, the book serves to offer a data-driven view of optimization, through the framework of memetic computation (MC). The authors provide a summary of the complete timeline of research activities in MC - beginning with the initiation of memes as local search heuristics hybridized with evolutionary algorithms, to their modern interpretation as computationally encoded building blocks of problem-solving knowledge that can be learned from one task and adaptively transmitted to another. In the light of recent research advances, the authors emphasize the further development of MC as a simultaneous problem learning and optimization paradigm with the potential to showcase human-like problem-solvingprowess; that is, by equipping optimization engines to acquire increasing levels of intelligence over time through embedded memes learned independently or via interactions. In other words, the adaptive utilization of available knowledge memes makes it possible for optimization engines to tailor custom search behaviors on the fly - thereby paving the way to general-purpose problem-solving ability (or artificial general intelligence). In this regard, the book explores some of the latest concepts from the optimization literature, including, the sequential transfer of knowledge across problems, multitasking, and large-scale (high dimensional) search, systematically discussing associated algorithmic developments that align with the general theme of memetics.The presented ideas are intended to be accessible to a wide audience of scientific researchers, engineers, students, and optimization practitioners who are familiar with the commonly used terminologies of evolutionary computation. A full appreciation of the mathematical formalizations and algorithmic contributions requires an elementary background in probability, statistics, and the concepts of machine learning. A prior knowledge of surrogate-assisted/Bayesian optimization techniques is useful, but not essential.