Refine
Year of publication
- 2018 (2811) (remove)
Document Type
- Article (1774)
- Postprint (286)
- Doctoral Thesis (284)
- Other (195)
- Review (111)
- Monograph/Edited Volume (59)
- Part of a Book (23)
- Working Paper (19)
- Part of Periodical (15)
- Conference Proceeding (13)
Language
Keywords
- climate change (18)
- gamma rays: general (17)
- Germany (12)
- German (11)
- cosmic rays (11)
- permafrost (11)
- stars: massive (11)
- ISM: supernova remnants (10)
- adaptation (10)
- inflammation (9)
Institute
- Institut für Biochemie und Biologie (326)
- Institut für Geowissenschaften (314)
- Institut für Physik und Astronomie (312)
- Institut für Chemie (194)
- Mathematisch-Naturwissenschaftliche Fakultät (134)
- Department Psychologie (124)
- Institut für Umweltwissenschaften und Geographie (94)
- Department Sport- und Gesundheitswissenschaften (92)
- Institut für Ernährungswissenschaft (92)
- Hasso-Plattner-Institut für Digital Engineering GmbH (89)
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.