Nearest Better Features

The Nearest-Better Features — sometimes also called Nearest-Better Clustering (NBC) Features — are based on a heuristic, which recognizes single peaks in a multimodal landscape. In general, the features are computed on two sets of distances: the distances from each point to its nearest neighbor and the distances from each point to its nearest-better neighbor. Here, the latter one is the closest observation (w.r.t. the reference point) with a better objective value than the reference point.

Based on these two distance sets, this feature set computes five NBC features. A further motivation of these features can be found, amongst others, in Kerschke et al. (2015).

Literature Reference

Preuss, M. (2012), “Improved Topological Niching for Real-Valued Global Optimization”, in Applications of Evolutionary Computation, pp. 386—395, Springer (http://dx.doi.org/10.1007/978-3-642-29178-4_39).

Kerschke, P. et al. (2015), “Detecting Funnel Structures by Means of Exploratory Landscape Analysis”, in Proceedings of the 17th Annual Conference on Genetic and Evolutionary Computation (GECCO ‘15), pp. 265-272, ACM (http://dx.doi.org/10.1145/2739480.2754642).