Resource-Aware Parameterizations of EDA Export

@proceedings{citeulike:4252263, abstract = {This paper presents a framework for the theoretical analysis of Estimation of Distribution Algorithms (EDA). Using this framework, derived from the VC-theory, we propose non-asymptotic bounds which depend on: 1) the population size, 2) the selection rate, 3) the families of distributions used for the modelling, 4) the dimension, and 5) the number of iterations. To validate these results, optimization algorithms are applied to a context where bounds on resources are crucial, namely Design of Experiments, that is a black-box optimization with very few fitness-values evaluations.}, author = {Gelly, S. and Teytaud, O. and Gagne, C.}, booktitle = {Evolutionary Computation, 2006. CEC 2006. IEEE Congress on}, citeulike-article-id = {4252263}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1688620}, journal = {Evolutionary Computation, 2006. CEC 2006. IEEE Congress on}, keywords = {algorithm-engineering, continuous, estimation-of-distribution-algorithm, evolutionary-computation, metaheuristic, parameter-setting}, pages = {2506--2512}, posted-at = {2009-04-01 09:54:12}, priority = {5}, title = {Resource-Aware Parameterizations of EDA}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1688620}, year = {2006} }

TY - CONF ID - citeulike:4252263 L3 - citeulike-article-id:4252263 N2 - This paper presents a framework for the theoretical analysis of Estimation of Distribution Algorithms (EDA). Using this framework, derived from the VC-theory, we propose non-asymptotic bounds which depend on: 1) the population size, 2) the selection rate, 3) the families of distributions used for the modelling, 4) the dimension, and 5) the number of iterations. To validate these results, optimization algorithms are applied to a context where bounds on resources are crucial, namely Design of Experiments, that is a black-box optimization with very few fitness-values evaluations. JF - Evolutionary Computation, 2006. CEC 2006. IEEE Congress on EP - 2512 TI - Resource-Aware Parameterizations of EDA T2 - Evolutionary Computation, 2006. CEC 2006. IEEE Congress on SP - 2506 KW - algorithm-engineering KW - continuous KW - estimation-of-distribution-algorithm KW - evolutionary-computation KW - metaheuristic KW - parameter-setting AU - Gelly, S AU - Teytaud, O AU - Gagne, C PY - 2006/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1688620 ER -