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Published in:
2011 | OriginalPaper | Chapter
Analysing the Adaptation Level of Parallel Hyperheuristics Applied to Mono-objective Optimisation Problems
Authors : Eduardo Segredo, Carlos Segura, Coromoto León
Published in: Nature Inspired Cooperative Strategies for Optimization (NICSO 2011)
Publisher: Springer Berlin Heidelberg
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Evolutionary Algorithms (
ea
s) are one of the most popular strategies for solving optimisation problems. One of the main drawbacks of
ea
s is the complexity of their parameter setting. This setting is mandatory to obtain high quality solutions. In order to deal with the parameterisation of an
ea
, hyperheuristics can be applied. They manage the choice of which parameters should be applied at each stage of the optimisation process. In this work, an analysis of the robustness of a parallel strategy that hybridises hyperheuristics, and parallel island-based models has been performed. Specifically, the model has been applied to a large set of mono-objective scalable benchmark problems with different landscape features. In addition, a study of the adaptation level of the proposal has been carried out. Computational results have shown the suitability of the model with every tested benchmark problem.