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Online model racing based on extreme performance.

TitleOnline model racing based on extreme performance.
Publication TypeConference Paper
Year of Publication2014
AuthorsZhang T, Georgiopoulos M, Anagnostopoulos GC
EditorArnold DV
Conference NameGenetic and Evolutionary Computation Conference, (GECCO '14), Vancouver, BC, Canada, July 12-16, 2014
PublisherAssociation for Computing Machinery (ACM)
Conference LocationVancouver, BC, Canada

Racing algorithms are often used for offline model selection, where models are compared in terms of their average performance over a collection of problems. In this paper, we present a new racing algorithm variant, Max-Race, which makes decisions based on the maximum performance of models. It is an online algorithm, whose goal is to optimally allocate computational resources in a portfolio of evolutionary algorithms, while solving a particular problem instance. It employs a hypothesis test based on extreme value theory in order to decide, which component algorithms to retire, while avoiding unnecessary computations. Experimental results confirm that Max-Race is able to identify the best individual with high precision and low computational overhead. When used as a scheme to select the best from a portfolio of algorithms, the results compare favorably to the ones of other popular algorithm portfolio approaches.


Nominated for Best Paper Award.

Acceptance rate 33% (180/544).


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