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Online model racing based on extreme performance.
|Title||Online model racing based on extreme performance.|
|Publication Type||Conference Paper|
|Year of Publication||2014|
|Authors||Zhang T, Georgiopoulos M, Anagnostopoulos GC|
|Conference Name||Genetic and Evolutionary Computation Conference, (GECCO '14), Vancouver, BC, Canada, July 12-16, 2014|
|Publisher||Association for Computing Machinery (ACM)|
|Conference Location||Vancouver, 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).