Hilbert curves for efficient exploratory landscape analysis neighbourhood sampling

TL;DR

This paper explores the use of Hilbert space-filling curves for efficient sampling and ordering in landscape analysis, demonstrating they are faster and equally effective compared to traditional methods like nearest neighbour and Latin hypercube sampling.

Contribution

It introduces Hilbert curves as a computationally efficient alternative for sampling and ordering in landscape analysis, improving speed without losing feature quality.

Findings

Hilbert curves provide uniform coverage and spatial correlation in samples.

They extract salient landscape features faster than traditional methods.

Hilbert-based ordering is more efficient than nearest neighbour ordering.

Abstract

Landscape analysis aims to characterise optimisation problems based on their objective (or fitness) function landscape properties. The problem search space is typically sampled, and various landscape features are estimated based on the samples. One particularly salient set of features is information content, which requires the samples to be sequences of neighbouring solutions, such that the local relationships between consecutive sample points are preserved. Generating such spatially correlated samples that also provide good search space coverage is challenging. It is therefore common to first obtain an unordered sample with good search space coverage, and then apply an ordering algorithm such as the nearest neighbour to minimise the distance between consecutive points in the sample. However, the nearest neighbour algorithm becomes computationally prohibitive in higher dimensions, thus there is a need for more efficient alternatives. In this study, Hilbert space-filling curves are proposed as a method to efficiently obtain high-quality ordered samples. Hilbert curves are a special case of fractal curves, and guarantee uniform coverage of a bounded search space while providing a spatially correlated sample. We study the effectiveness of Hilbert curves as samplers, and discover that they are capable of extracting salient features at a fraction of the computational cost compared to Latin hypercube sampling with post-factum ordering. Further, we investigate the use of Hilbert curves as an ordering strategy, and find that they order the sample significantly faster than the nearest neighbour ordering, without sacrificing the saliency of the extracted features.