A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes

TL;DR

This paper introduces deep learning-based, feature-free methods for characterizing single-objective continuous optimization landscapes, offering comparable performance to traditional features while providing new representations like point clouds and images.

Contribution

It proposes novel, feature-free landscape characterization techniques using deep learning on alternative data representations, enhancing problem understanding without relying on traditional features.

Findings

Methods perform on par with traditional landscape features.

Deep learning accurately predicts landscape properties.

Approaches are validated on the BBOB testbed.

Abstract

Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucial both for problem understanding as well as for assessing benchmark set diversity and composition. Despite the irrefutable usefulness of these features, they suffer from their own ailments and downsides. Hence, in this work we provide a collection of different approaches to characterize optimization landscapes. Similar to conventional landscape features, we require a small initial sample. However, instead of computing features based on that sample, we develop alternative representations of the original sample. These range from point clouds to 2D images and, therefore, are entirely feature-free. We demonstrate and validate our devised methods on the BBOB testbed and predict, with the help of Deep Learning, the high-level, expert-based landscape properties such as the degree of multimodality and the existence of funnel structures. The quality of our approaches is on par with methods relying on the traditional landscape features. Thereby, we provide an exciting new perspective on every research area which utilizes problem information such as problem understanding and algorithm design as well as automated algorithm configuration and selection.