Landscape analysis is of fundamental interest for improving our understanding on the behavior of evolutionary search, and for developing general-purpose automated solvers based on techniques from statistics and machine learning. In this paper, we push a step towards the development of a landscape-aware approach by proposing a set of landscape features for multi-objective combinatorial optimization, by decomposing the original multi-objective problem into a set of single-objective sub-problems. Based on a comprehensive set of bi-objective ρmnk-landscapes and three variants of the state-of-the-art Moea/d algorithm, we study the association between the proposed features, the global properties of the considered landscapes, and algorithm performance. We also show that decomposition-based features can be integrated into an automated approach for predicting algorithm performance and selecting the most accurate one on blind instances. In particular, our study reveals that such a landscape-aware approach is substantially better than the single best solver computed over the three considered Moea/d variants.

The design of effective features enabling the development of automated landscape-aware techniques requires to address a number of inter-dependent issues. In this paper, we are interested in contrasting the amount of budget devoted to the computation of features with respect to: (i) the effectiveness of the features in grasping the characteristics of the landscape, and (ii) the gain in accuracy when solving an unknown problem instance by means of a featureinformed automated algorithm selection approach. We consider multi-objective combinatorial landscapes where, to the best of our knowledge, no in depth investigations have been conducted so far. We study simple cost-adjustable sampling strategies for extracting different state-of-the-art features. Based on extensive experiments, we report a comprehensive analysis on the impact of sampling on landscape feature values, and the subsequent automated algorithm selection task. In particular, we identify different global trends of feature values leading to non-trivial cost-vs-accuracy trade-off(s). Besides, we provide evidence that the sampling strategy can improve the prediction accuracy of automated algorithm selection. Interestingly, this holds independently of whether the sampling cost is taken into account or not in the overall solving budget. CCS CONCEPTS• Applied computing → Multi-criterion optimization and decisionmaking; • Theory of computation → Evolutionary algorithms.

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