Cost-vs-accuracy of sampling in multi-objective combinatorial exploratory landscape analysis - Archive ouverte HAL Access content directly
Conference Papers Year : 2022

Cost-vs-accuracy of sampling in multi-objective combinatorial exploratory landscape analysis

Abstract

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 feature-informed 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.
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Dates and versions

hal-03693659 , version 1 (02-03-2023)

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Raphaël Cosson, Bilel Derbel, Arnaud Liefooghe, Sébastien Vérel, Hernán E. Aguirre, et al.. Cost-vs-accuracy of sampling in multi-objective combinatorial exploratory landscape analysis. GECCO 2022 - Genetic and Evolutionary Computation Conference, Jul 2022, Boston, MA, United States. pp.493-501, ⟨10.1145/3512290.3528731⟩. ⟨hal-03693659⟩
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