Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco

This paper provides an introduction into to the R-package flacco. A slightly modified version of this document has recently been submitted to the Journal of Statistical Software and is currently under review. Choosing the best-performing optimizer(s) out of a portfolio of optimization algorithms is usually a difficult and complex task. It gets even worse, if the underlying functions are unknown, i.e., so-called Black-Box problems, and function evaluations are considered to be expensive. In the case of continuous single-objective optimization problems, Exploratory Landscape Analysis (ELA) – a sophisticated and effective approach for characterizing the landscapes of such problems by means of numerical values before actually performing the optimization task itself – is advantageous. Unfortunately, until now it has been quite complicated to compute multiple ELA features simultaneously, as the corresponding code has been – if at all – spread across multiple platforms or at least across several packages within these platforms. This article presents a broad summary of existing ELA approaches and introduces flacco, an R-package for feature-based landscape analysis of continuous and constrained optimization problems. Although its functions neither solve the optimization problem itself nor the related Algorithm Selection Problem (ASP), it offers easy access to an essential ingredient of the ASP by providing a wide collection of ELA features on a single platform – even within a single package. In addition, flacco provides multiple visualization techniques, which enhance the understanding of some of these numerical features, and thereby make certain landscape properties more comprehensible. On top of that, we will introduce the package’s build-in, as well as web-hosted and hence platform-independent, graphical user interface (GUI), which facilitates the usage of the package – especially for people who are not familiar with R – making it a very convenient toolbox when working towards algorithm selection of continuous single-objective optimization problems.