Synthetic vs. Real-World Continuous Landscapes: A Local Optima Networks View

Citation

Contreras-Cruz MA, Ochoa G & Ramirez-Paredes JP (2020) Synthetic vs. Real-World Continuous Landscapes: A Local Optima Networks View. In: Filipič B, Minisci E & Vasile M (eds.) Bioinspired Optimization Methods and Their Applications. Lecture Notes in Computer Science, 12438. 9th International Conference, BIOMA 2020, Brussels, Belgium, 19.11.2020-20.11.2020. Cham, Switzerland: Springer International Publishing, pp. 3-16. https://doi.org/10.1007/978-3-030-63710-1_1

Abstract
Local optima networks (LONs) are a useful tool to analyse and visualise the global structure of fitness landscapes. The main goal of our study is to use LONs to contrast the global structure of synthetic benchmark functions against those of real-world continuous optimisation problems of similar dimensions. We selected two real-world problems, namely, an engineering design problem and a machine learning problem. Our results indicate striking differences in the global structure of synthetic vs real-world problems. The real-world problems studied were easier to solve than the synthetic ones, and our analysis reveals why; they have easier to traverse global structures with fewer nodes and edges, no sub-optimal funnels, higher neutrality and multiple global optima with shorter trajectories towards them.

Keywords
Local optima networks Funnel structures; Fitness landscapes; Real-world optimisation problems

People

Professor Gabriela Ochoa

Professor, Computing Science