Empirical study on meta-feature characterization for multi-objective optimization problems

Algorithm recommendation based on meta-learning was studied previously. The research on the meta-features extraction, which is a key for the success of recommendation, is lacking for multi-objective optimization problems (MOPs). This paper proposes four sets of meta-features to characterize MOPs. In addition, the algorithm recommendation model based on meta-learning is extended to the field of multi-objective optimization. To evaluate the efficiency and effectiveness of the extracted meta-features, 29 MOPs benchmark functions with different dimensions and two real-world MOPs are employed for comprehensive comparison. Experimental results show that the proposed meta-features in this paper can fully characterize MOPs and are empirically efficient for algorithm recommendation.