Abstract
The quality of a heuristic optimization algorithm is strongly dependent on its parameter values. Finding the optimal parameter values is a laborious task which requires expertise and knowledge about the algorithm, its parameters and the problem. This paper describes, how the optimization of parameters can be automated by using another optimization algorithm on a meta-level. To demonstrate this, a meta-optimization problem which is algorithm independent and allows any kind of algorithm on the meta- and base-level is implemented for the open source optimization environment HeuristicLab. Experimental results of the optimization of a genetic algorithm for different sets of base-level problems with different complexities are shown.
The work described in this paper was done within the Josef Ressel Centre for Heuristic Optimization Heureka! ( http://heureka.heuristiclab.com/ ) sponsored by the Austrian Research Promotion Agency (FFG).
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Neumüller, C., Wagner, S., Kronberger, G., Affenzeller, M. (2012). Parameter Meta-optimization of Metaheuristic Optimization Algorithms. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_47
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DOI: https://doi.org/10.1007/978-3-642-27549-4_47
Publisher Name: Springer, Berlin, Heidelberg
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