Landscape characterization of numerical optimization problems using biased scattered data

Abstract

The characterization of optimization problems over continuous parameter spaces plays an important role in optimization. A form of "fitness landscape" analysis is often carried out to describe the problem space in terms of modality, smoothness and variable separability. The outcomes of this analysis can then be used as a measure of problem difficulty and to predict the behaviour of a given algorithm. However, the metric value estimates of the landscape characterization are dependent upon the representation scheme adopted and the sampling method used. Consequently, the development of a complete classification of problem structure and complexity has proven to be challenging. In this paper, we c..