@conference {33, title = {Online model racing based on extreme performance.}, booktitle = {Genetic and Evolutionary Computation Conference, (GECCO {\textquoteright}14), Vancouver, BC, Canada, July 12-16, 2014}, year = {2014}, note = {

Nominated for Best Paper Award.

Acceptance rate 33\% (180/544).

}, publisher = {Association for Computing Machinery (ACM)}, organization = {Association for Computing Machinery (ACM)}, address = {Vancouver, BC, Canada}, 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.

}, doi = {10.1145/2576768.2598336}, url = {http://doi.acm.org/10.1145/2576768.2598336}, author = {Tiantian Zhang and Michael Georgiopoulos and Georgios C. Anagnostopoulos}, editor = {Dirk V. Arnold} }