Adaptive global optimization with local search
Adaptive global optimization with local search
Memetic algorithms: a short introduction
New ideas in optimization
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Advanced fitness landscape analysis and the performance of memetic algorithms
Evolutionary Computation - Special issue on magnetic algorithms
On the analysis of the (1+1) memetic algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Computational complexity and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Memetic algorithms with variable-depth search to overcome local optima
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Analyses of simple hybrid algorithms for the vertex cover problem*
Evolutionary Computation
The impact of parametrization in memetic evolutionary algorithms
Theoretical Computer Science
Computational complexity and evolutionary computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Computational complexity and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Hybridizing evolutionary algorithms with opportunistic local search
Proceedings of the 15th annual conference on Genetic and evolutionary computation
An intelligent multi-restart memetic algorithm for box constrained global optimisation
Evolutionary Computation
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A popular approach in the design of evolutionary algorithms is to integrate local search into the random search process. These so-called memetic algorithms have demonstrated their efficiency in countless applications covering a wide area of practical problems. However, theory of memetic algorithms is still in its infancy and there is a strong need for a rigorous theoretical foundation to better understand these heuristics. Here, we attack one of the fundamental issues in the design of memetic algorithms from a theoretical perspective, namely the choice of the frequency with which local search is applied. Since no guidelines are known for the choice of this parameter, we care about its impact on memetic algorithm performance. We present worst-case problems where the local search frequency has an enormous impact on the performance of a simple memetic algorithm. A rigorous theoretical analysis shows that on these problems, with overwhelming probability, even a small factor of 2 decides about polynomial versus exponential optimization times.