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Choosing the best-performing optimizer(s) out of a portfolio of optimizationalgorithms is usually a difficult and complex task. It gets even worse, if theunderlying functions are unknown, i.e., so-called Black-Box problems, andfunction evaluations are considered to be expensive. In the case of continuoussingle-objective optimization problems, Exploratory Landscape Analysis (ELA) -a sophisticated and effective approach for characterizing the landscapes ofsuch problems by means of numerical values before actually performing theoptimization task itself - is advantageous. Unfortunately, until now it hasbeen quite complicated to compute multiple ELA features simultaneously, as thecorresponding code has been - if at all - spread across multiple platforms orat least across several packages within these platforms.  This article presents a broad summary of existing ELA approaches andintroduces flacco, an R-package for feature-based landscape analysis ofcontinuous and constrained optimization problems. Although its functionsneither solve the optimization problem itself nor the related "AlgorithmSelection Problem (ASP)", it offers easy access to an essential ingredient ofthe ASP by providing a wide collection of ELA features on a single platform -even within a single package. In addition, flacco provides multiplevisualization techniques, which enhance the understanding of some of thesenumerical features, and thereby make certain landscape properties morecomprehensible. On top of that, we will introduce the package's build-in, aswell as web-hosted and hence platform-independent, graphical user interface(GUI), which facilitates the usage of the package - especially for people whoare not familiar with R - making it a very convenient toolbox when workingtowards algorithm selection of continuous single-objective optimizationproblems.

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