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https://hdl.handle.net/2440/113404
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Type: | Journal article |
Title: | Reoptimization Time Analysis of Evolutionary Algorithms on Linear Functions Under Dynamic Uniform Constraints |
Author: | Shi, F. Schirneck, M. Friedrich, T. Kötzing, T. Neumann, F. |
Citation: | Algorithmica: an international journal in computer science, 2019; 81(2):1-30 |
Publisher: | Springer |
Issue Date: | 2019 |
ISSN: | 0178-4617 1432-0541 |
Statement of Responsibility: | Feng Shi, Martin Schirneck, Tobias Friedrich, Timo Kötzing, Frank Neumann |
Abstract: | 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. |
Keywords: | Evolutionary algorithm Runtime analysis Reoptimization time Dynamic constraint Uniform constraint |
Rights: | © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
DOI: | 10.1007/s00453-018-0451-4 |
Grant ID: | http://purl.org/au-research/grants/arc/DP140103400 http://purl.org/au-research/grants/arc/DP160102401 |
Published version: | http://dx.doi.org/10.1007/s00453-018-0451-4 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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