Volltext-Downloads (blau) und Frontdoor-Views (grau)
  • search hit 1 of 2
Back to Result List

Overview: Evolutionary Algorithms

  • Evolutionary algorithm (EA) is an umbrella term used to describe population-based stochastic direct search algorithms that in some sense mimic natural evolution. Prominent representatives of such algorithms are genetic algorithms, evolution strategies, evolutionary programming, and genetic programming. On the basis of the evolutionary cycle, similarities and differences between these algorithms are described. We briefly discuss how EAs can be adapted to work well in case of multiple objectives, and dynamic or noisy optimization problems. We look at the tuning of algorithms and present some recent developments coming from theory. Finally, typical applications of EAs to real-world problems are shown, with special emphasis on data-mining applications

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Author:Thomas Bartz-BeielsteinGND, Jürgen Branke, Jörn Mehnen, Olaf Mersmann
URN:urn:nbn:de:hbz:832-cos-777
Series (Serial Number):CIplus (2/2015)
Document Type:Report
Language:German
Year of Completion:2015
Release Date:2015/02/20
Tag:Evolutionsstrategien; Evolutionäre Algorithmen; Genetische Algorithmen; Genetisches Programmieren
Evolution Strategies; Evolutionary Algorithms; Genetic Algorithms; Genetic programming
GND Keyword:Evolutionsstrategie; Evolutionärer Algorithmus; Metaheuristik; Optimierung; Optimierungsproblem; Soft Computing; Versuchsplanung
Contributor:Bartz-Beielstein, Thomas
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10) / Fakultät 10 / Institut für Informatik
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
Open Access:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung