table constants for test problem - - - - - - - - - - the best known minimal value in [ was - in [ better solution was found- using genetic algorithm the corresponding solution vector was completely different from the one in [ further improvement,- was found in [ ]using the ce methodgiving similar solution vector to that in [ ] to obtain solution with scowe first converted this -dimensional problem into -dimensional one by defining the objective function (ys ( )where and ( ) ( ) ( )subject to where the {xi are taken as functions of the {yi we then adopted penalty approach (see section by adding penalty function to the original objective functionse (ys ( = max{-(xi ) }whereagainthe {xi are defined in terms of the {yi as above optimizing this last function with scowe foundin less time than the other algorithmsa slightly smaller function value- with solution vector in line with the earlier solutions noisy optimization in noisy optimizationthe objective function is unknownbut estimates of function values are availablee via simulation for exampleto find an optimal prediction function in supervised learningthe exact risk `(ge loss(yg( )is usually unknown and only estimates of the risk are available optimizing the risk is thus typically noisy optimization problem noisy optimization features prominently in simulation studies where noisy optimization