Towards an FCA-based Recommender System for Black-Box Optimization

Josefine AsmusJosefine Asmus, Daniel BorchmannDaniel Borchmann, Ivo F. SbalzariniIvo F. Sbalzarini, Dirk WaltherDirk Walther

Download

Josefine Asmus, Daniel Borchmann, Ivo F. Sbalzarini, Dirk Walther
Towards an FCA-based Recommender System for Black-Box Optimization
In Sergei O. Kuznetsov and Amedeo Napoli and Sebastian Rudolph, eds., Proceedings of the 3rd International Workshop on "What can FCA do for Artificial Intelligence?" (FCA4AI'14), volume 1257 of CEUR Workshop Proceedings, 35-42, 2014

Details
Bibtex

KurzfassungAbstract
Black-box optimization problems are of practical importance throughout science and engineering. Hundreds of algorithms and heuristics have been developed to solve them. However, none of them outperforms 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 problem. 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 further outline the use of attribute exploration to identify problem features that predict algorithm performance.
Forschungsgruppe:Research Group: AutomatentheorieAutomata Theory

@inproceedings{ AsBoSbWa-FCA4AI-14,
  author = {Josefine {Asmus} and Daniel {Borchmann} and Ivo F. {Sbalzarini} and Dirk {Walther}},
  booktitle = {Proceedings of the 3rd International Workshop on "What can FCA do for Artificial Intelligence?" ({FCA4AI'14})},
  editor = {Sergei O. {Kuznetsov} and Amedeo {Napoli} and Sebastian {Rudolph}},
  pages = {35--42},
  series = {CEUR Workshop Proceedings},
  title = {Towards an FCA-based Recommender System for Black-Box Optimization},
  volume = {1257},
  year = {2014},
}