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Christian Hanster, Pascal Kerschke · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2017
Finding the optimal solution for a given problem has always been an intriguing goal and a key for reaching this goal is sound knowledge of the problem at hand. In case of single-objective, continuous, global optimization problems, such knowledge can be gained by Exploratory Landscape Analysis (ELA), which computes features that quantify the problem's landscape prior to optimization. Due to the various backgrounds of researches that developed such features, there nowadays exist numerous implementations of feature sets across multiple programming languages, which is a blessing and burden at the same time.