Open AccessThe ICON Challenge on Algorithm Selection
Author(s) -
Kotthoff Lars,
Hurley Barry,
O'Sullivan Barry
Publication year - 2017
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v38i2.2722
Subject(s) - icon , selection (genetic algorithm) , variety (cybernetics) , relevance (law) , computer science , machine learning , algorithm , selection algorithm , artificial intelligence , data mining , political science , law , programming language
Algorithm selection is of increasing practical relevance in a variety of applications. Many approaches have been proposed in the literature, but their evaluations are often not comparable, making it hard to judge which approaches work best. The ICON Challenge on Algorithm Selection objectively evaluated many prominent approaches from the literature, making them directly comparable for the first time. The results show that there is still room for improvement, even for the very best approaches.