Title

Online Model Racing Based On Extreme Performance

Keywords

Algorithm portfolio; Extreme value theory; Online model selection; Racing algorithm

Abstract

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. © 2014 is held by the owner/author(s).

Publication Date

1-1-2014

Publication Title

GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference

Number of Pages

1351-1358

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2576768.2598336

Socpus ID

84905683998 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84905683998

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