DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis

Solving real-world black-box optimization problems can be extremely complicated, particularly if they are strongly nonlinear and require expensive function evaluations. As suggested by the no free lunch theorem in [1], there is no such things as a single-best optimization algorithm, that is capable of optimally solving all kind of problems. The task in identifying the most time-and resource-efficient optimization algorithms for each specific problem, also known as the algorithm selection problem (ASP) (see [2]), is tedious and challenging, even with domain knowledge and experience. In recent years, landscape-aware algorithm selection has gained increasing attention from the research community, where the fitness landscape characteristics are exploited to explain the effectiveness of an algorithm across different problem instances (see [3, 4]). Beyond that, it has been shown that landscape characteristics are sufficiently informative in reliably predicting the performance of optimization algorithms, e.g., using Machine Learning approaches (see [5-10]). In other words, the expected performance of an optimization algorithm on an unseen problem can be estimated, once the corresponding landscape characteristics have been identified. Interested readers are referred to [7, 11-14].