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Erschienen in:
2012 | OriginalPaper | Buchkapitel
A Non-adaptive Stochastic Local Search Algorithm for the CHeSC 2011 Competition
verfasst von : Franco Mascia, Thomas Stützle
Erschienen in: Learning and Intelligent Optimization
Verlag: Springer Berlin Heidelberg
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In this work, we present our submission for the Cross-domain Heuristic Search Challenge 2011. We implemented a stochastic local search algorithm that consists of several algorithm schemata that have been offline-tuned on four sample problem domains. The schemata are based on all families of low-level heuristics available in the framework used in the competition with the exception of crossover heuristics. Our algorithm goes through an initial phase that filters dominated low-level heuristics, followed by an algorithm schemata selection implemented in a race. The winning schema is run for the remaining computation time. Our algorithm ranked seventh in the competition results. In this paper, we present the results obtained after a more careful tuning, and a different combination of algorithm schemata included in the final algorithm design. This improved version would rank fourth in the competition.