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

Research output: Contribution to journalConference articleContributedpeer-review

Contributors

Abstract

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.

Details

Original languageEnglish
Pages (from-to)35-42
Number of pages8
JournalCEUR Workshop Proceedings
Volume1257
Publication statusPublished - 2014
Peer-reviewedYes

Conference

Title3rd International Workshop "What can FCA do for Artificial Intelligence?"
Abbreviated titleFCA4AI 2014
DescriptionCo-Located with the European Conference on Artificial Intelligence, ECAI 2014
Duration19 August 2014
CityPrague
CountryCzech Republic

External IDs

ORCID /0000-0003-4414-4340/work/142252137

Keywords

ASJC Scopus subject areas