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|Title:||Reoptimization Time Analysis of Evolutionary Algorithms on Linear Functions Under Dynamic Uniform Constraints|
|Citation:||Algorithmica, 2018; :1-30|
|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|
|Appears in Collections:||Computer Science publications|
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