Reoptimization time analysis of evolutionary algorithms on linear functions under dynamic uniform constraints
- Rigorous runtime analysis is a major approach towards understanding evolutionary computing techniques, and in this area linear pseudo-Boolean objective functions play a central role. Having an additional linear constraint is then equivalent to the NP-hard Knapsack problem, certain classes thereof have been studied in recent works. In this article, we present a dynamic model of optimizing linear functions under uniform constraints. Starting from an optimal solution with respect to a given constraint bound, we investigate the runtimes that different evolutionary algorithms need to recompute an optimal solution when the constraint bound changes by a certain amount. The classical (1+1) EA and several population-based algorithms are designed for that purpose, and are shown to recompute efficiently. Furthermore, a variant of the (1+(λ,λ))GA for the dynamic optimization problem is studied, whose performance is better when the change of the constraint bound is small.
Author details: | Feng ShiORCiD, Friedrich Martin SchirneckORCiDGND, Tobias FriedrichORCiDGND, Timo KötzingORCiD, Frank NeumannORCiD |
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URN: | urn:nbn:de:kobv:517-opus4-605295 |
DOI: | https://doi.org/10.1007/s00453-020-00739-x |
ISSN: | 0178-4617 |
ISSN: | 1432-0541 |
Title of parent work (English): | Algorithmica : an international journal in computer science |
Publisher: | Springer |
Place of publishing: | New York |
Publication type: | Article |
Language: | English |
Date of first publication: | 2018/05/10 |
Publication year: | 2018 |
Release date: | 2024/01/11 |
Volume: | 82 |
Issue: | 10 |
Number of pages: | 7 |
First page: | 3117 |
Last Page: | 3123 |
Organizational units: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
Peer review: | Referiert |