Abstract

Changes in dynamic optimization problems entail updates to the problem model, which in turn can result in changes to the problem's fitness landscape and even its solution encoding. In order to yield valid solutions that are applicable to the current problem state, optimization algorithms must be able to cope with such dynamic problem updates. Furthermore, depending on the optimization use case, changes occurring in real-world environments require an optimizer to adapt to changing process conditions and yield updated, valid solutions within a short time frame. In this paper, dynamic problem changes and their effects on an optimizer's algorithmic behavior are studied in the context of crane scheduling. Three open-ended versions of RAPGA, a relevant alleles preserving genetic algorithm, are evaluated, some of which include self-adaption and a special treatment of certain events that require domain knowledge to be recognized. The proposed extensions affect the algorithm behavior as desired. On the one hand, the algorithms converge faster after a loss in solution quality is detected. On the other hand, new genetic material is introduced, making it possible to reach high quality areas of the search space again.
OriginalspracheEnglisch
TitelComputer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers
Redakteure/-innenRoberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia
ErscheinungsortCham
Herausgeber (Verlag)Springer
Seiten61-68
Seitenumfang8
ISBN (Print)9783031253119
DOIs
PublikationsstatusVeröffentlicht - 10 Feb. 2023

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band13789 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

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