Towards an FCA-based Recommender System for Black-Box Optimization
Josefine Asmus, Daniel Borchmann, Ivo F. Sbalzarini, Dirk Walther · 2014
Abstract. Black-box optimization problems are of practical importance throughout science and engineering. Hundreds of algorithms and heuris-tics have been developed to solve them. However, none of them outper-forms any other on all problems. The success of a particular heuristic is always relative to a class of problems. So far, these problem classes are elusive and it is not known what algorithm to use on a given prob-lem. Here we describe the use of Formal Concept Analysis (FCA) to extract implications about problem classes and algorithm performance from databases of empirical benchmarks. We explain the idea in a small example and show that FCA produces meaningful implications. We fur-ther outline the use of attribute exploration to identify problem features that predict algorithm performance. 1