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Adaptive Selection of Helper-Objectives with Reinforcement Learning

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2 Author(s)

In this paper a previously proposed method of choosing auxiliary fitness functions is applied to adaptive selection of helper-objectives. Helper-objectives are used in evolutionary computation to enhance the optimization of the primary objective. The method based on choosing between objectives of a single-objective evolutionary algorithm with reinforcement learning is briefly described. It is tested on a model problem. From the results of the experiment, it can be concluded that the method allows to automatically select the most effective helper-objectives and ignore the ineffective ones. It is also shown that the proposed method outperforms multi-objective evolutionary algorithms, that were used with helper-objectives originally.

Published in:

Machine Learning and Applications (ICMLA), 2012 11th International Conference on  (Volume:2 )

Date of Conference:

12-15 Dec. 2012