Local Search in Evolutionary Algorithms: The Impact of the Local Search Frequency
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.
KeywordsLocal Search Evolutionary Algorithm Memetic Algorithm Search Point Overwhelming Probability
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- 2.Hart, W.E.: Adaptive Global Optimization with Local Search. PhD thesis, University of California, San Diego, CA (1994)Google Scholar
- 3.Hart, W.E., Krasnogor, N., Smith, J.E. (eds.): Recent Advances in Memetic Algorithms. Studies in Fuzziness and Soft Computing, vol. 166. Springer, Heidelberg (2004)Google Scholar
- 4.Lourenço, H.R., Martin, O., Stützle, T.: Iterated local search. In: Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 57, pp. 321–353. Kluwer Academic Publishers, Norwell (2002)Google Scholar
- 6.Moscato, P.: Memetic algorithms: a short introduction. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 219–234. McGraw-Hill, New York (1999)Google Scholar
- 7.Scheideler, C.: Probabilistic Methods for Coordination Problems. HNI-Verlagsschriftenreihe 78, University of Paderborn, Habilitation Thesis (2000), available at http://www14.in.tum.de/personen/scheideler/index.html.en
- 8.Sinha, A., Chen, Y., Goldberg, D.E.: Designing efficient genetic and evolutionary algorithm hybrids. In: , pp. 259–288Google Scholar
- 9.Sudholt, D.: Local search in memetic algorithms: the impact of the local search frequency. Technical Report CI-208/06, Collaborative Research Center 531, University of Dortmund (June 2006), available at http://sfbci.cs.uni-dortmund.de