Towards an FCA-based Recommender System for Black-Box Optimization
Research output: Contribution to journal › Conference article › Contributed › peer-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 language | English |
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Pages (from-to) | 35-42 |
Number of pages | 8 |
Journal | CEUR Workshop Proceedings |
Volume | 1257 |
Publication status | Published - 2014 |
Peer-reviewed | Yes |
Conference
Title | 3rd International Workshop "What can FCA do for Artificial Intelligence?" |
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Abbreviated title | FCA4AI 2014 |
Description | Co-Located with the European Conference on Artificial Intelligence, ECAI 2014 |
Duration | 19 August 2014 |
City | Prague |
Country | Czech Republic |
External IDs
ORCID | /0000-0003-4414-4340/work/142252137 |
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