Format:
Online-Ressource (XIX, 381 p)
,
digital
Edition:
Springer eBook Collection. Computer Science
ISBN:
9783662043783
Series Statement:
Natural Computing Series
Content:
Evolutionary Algorithms, in particular Evolution Strategies, Genetic Algorithms, or Evolutionary Programming, have found wide acceptance as robust optimization algorithms in the last ten years. Compared with the broad propagation and the resulting practical prosperity in different scientific fields, the theory has not progressed as much. This monograph provides the framework and the first steps toward the theoretical analysis of Evolution Strategies (ES). The main emphasis is on understanding the functioning of these probabilistic optimization algorithms in real-valued search spaces by investigating the dynamical properties of some well-established ES algorithms. The book introduces the basic concepts of this analysis, such as progress rate, quality gain, and self-adaptation response, and describes how to calculate these quantities. Based on the analysis, functioning principles are derived, aiming at a qualitative understanding of why and how ES algorithms work
Additional Edition:
ISBN 9783642086700
Additional Edition:
Erscheint auch als Druck-Ausgabe ISBN 9783642086700
Additional Edition:
Erscheint auch als Druck-Ausgabe ISBN 9783540672975
Additional Edition:
Erscheint auch als Druck-Ausgabe ISBN 9783662043790
Language:
English
DOI:
10.1007/978-3-662-04378-3
URL:
Volltext
(lizenzpflichtig)
