Refine
Has Fulltext
- no (11)
Language
- English (11)
Is part of the Bibliography
- yes (11)
Keywords
- Theory (2)
- genetic programming (2)
- theory (2)
- Ant colony optimization (1)
- Drift (1)
- Estimation-of-distribution algorithm (1)
- Evolutionary computation (1)
- First hitting time (1)
- First-hitting time (1)
- Fitness level method (1)
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.