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Applying Evolutionary Algorithms Successfully: A Guide Gained from Real-world Applications

Jakob, Wilfried


Metaheuristics (MHs) in general and Evolutionary Algorithms (EAs) in particular are well known tools for successful optimization of difficult problems. But when is their application meaningful and how does one approach such a project as a novice? How do you avoid beginner's mistakes or use the design possibilities of a metaheuristic search as efficiently as possible? This paper tries to give answers to these questions based on 30 years of research and application of the Evolutionary Algorithm GLEAM and its memetic extension HyGLEAM. Most of the experience gathered and discussed here can also be applied to the use of other metaheuristics such as ant algorithms or particle swarm optimization.
This paper addresses users with basic knowledge of MHs in general and EAs in particular who want to apply them in an optimization project. For this purpose, a number of questions that arise in the course of such a project are addressed. At the end, some non-technical project management issues are discussed, whose importance for project success is often underestimated.

Volltext §
DOI: 10.5445/IR/1000135763
Veröffentlicht am 22.07.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2194-1629
KITopen-ID: 1000135763
Verlag Karlsruher Institut für Technologie (KIT)
Umfang 26 S.
Serie KIT Scientific Working Papers ; 170
Schlagwörter evolutionary algorithms; memetic algorithms; metaheuristics; real-world applications; application guide; application experiences; global optimization
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