@conference {32, title = {Derivative free optimization using a population-based stochastic gradient estimator.}, 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 = {

In this paper we introduce a derivative-free optimization method that is derived from a population based stochastic gradient estimator. We first demonstrate some properties of this estimator and show how it is expected to always yield a descent direction. We analytically show that the difference between the expected function value and the optimum decreases exponentially for strongly convex functions and the expected distance between the current point and the optimum has an upper bound. Then we experimentally tune the parameters of our algorithm to get the best performance. Finally, we use the Black-Box-Optimization-Benchmarking test function suite to evaluate the performance of the algorithm. The experiments indicate that the method offer notable performance advantages especially, when applied to objective functions that are ill-conditioned and potentially multi-modal. This result, coupled with the low computational cost when compared to Quasi-Newton methods, makes it quite attractive.

}, doi = {10.1145/2576768.2598365}, url = {http://doi.acm.org/10.1145/2576768.2598365}, author = {Azhar Khayrattee and Georgios C. Anagnostopoulos}, editor = {Dirk V. Arnold} } @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} }