@conference {35, title = {S-Race: A Multi-Objective Racing Algorithm}, booktitle = {Genetic \& Evolutionary Computation Conference (GECCO)}, year = {2013}, note = {

Finalist for Best Paper Award.

Acceptance rate 36\% (204/570)

}, publisher = {Association for Computing Machinery (ACM)}, organization = {Association for Computing Machinery (ACM)}, address = {Amsterdam, The Netherlands}, abstract = {

This paper presents a multi-objective racing algorithm, S-Race, which efficiently addresses multi-objective model selection problems in the sense of Pareto optimality. As a racing algorithm, S-Race attempts to eliminate candidate models as soon as there is sufficient statistical evidence of their inferiority relative to other models with respect to all objectives. This approach is followed in the interest of controlling the computational effort. S-Race adopts a non-parametric sign test to identify pair-wise domination relationship between models. Meanwhile, Holm{\textquoteright}s Step-Down method is employed to control the overall family-wise error rate of simultaneous hypotheses testing during the race. Experimental results involving the selection of superior Support Vector Machine classifiers according to 2 and 3 performance criteria indicate that S-Race is an efficient and effective algorithm for automatic model selection, when compared to a brute-force, multi-objective selection approach.

}, doi = {10.1145/2463372.2463561}, author = {Tiantian Zhang and Michael Georgiopoulos and Georgios C. Anagnostopoulos}, editor = {Christian Blum and Enrique Alba} } @conference {45, title = {A Winner-take-all Methodology: Finding the Best Evolutionary Algorithm for the Global Optimization of Functions}, booktitle = {Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation}, year = {2012}, note = {

Acceptance rate 37\% (172/467).

}, publisher = {ACM}, organization = {ACM}, address = {Philadelphia, Pennsylvania, USA}, keywords = {differential evolution, particle swarm optimization, single-objective optimization}, isbn = {978-1-4503-1178-6}, doi = {10.1145/2330784.2331021}, url = {http://doi.acm.org/10.1145/2330784.2331021}, author = {Tiantian Zhang and Giselle Borrero and Michael Georgiopoulos} }