Universal alignment probabilities and subset selection for ordinal optimization
Journal of Optimization Theory and Applications
Universal alignment probability revisited
Journal of Optimization Theory and Applications
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A stochastic approach to hotel revenue optimization
Computers and Operations Research
Ordinal Optimization: Soft Computing for Hard Problems (International Series on Discrete Event Dynamic Systems)
A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A framework for memetic optimization using variable global and local surrogate models
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
An efficient hybrid algorithm for resource-constrained project scheduling
Information Sciences: an International Journal
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
Improved computation for Levenberg-Marquardt training
IEEE Transactions on Neural Networks
A memetic algorithm for extending wireless sensor network lifetime
Information Sciences: an International Journal
Stochastic Simulation Optimization: An Optimal Computing Budget Allocation
Stochastic Simulation Optimization: An Optimal Computing Budget Allocation
Computers and Operations Research
Entropy-based efficiency enhancement techniques for evolutionary algorithms
Information Sciences: an International Journal
Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation
IEEE Transactions on Evolutionary Computation
Coevolution of Fitness Predictors
IEEE Transactions on Evolutionary Computation
Application of an Ordinal Optimization Algorithm to the Wafer Testing Process
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Approximation capability in C(R¯n) by multilayer feedforward networks and related problems
IEEE Transactions on Neural Networks
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This work proposes an evolutionary algorithm (EA) that is assisted by a surrogate model in the framework of ordinal optimization (OO) and optimal computing budget allocation (OCBA) for use in solving the real-time combinatorial stochastic simulation optimization problem with a huge discrete solution space. For real-time applications, an off-line trained artificial neural network (ANN) is utilized as the surrogate model. EA, assisted by the trained ANN, is applied to the problem of interest to obtain a subset of good enough solutions, S. Also for real-time application, the OCBA technique is used to find the best solution in S, and this is the obtained good enough solution. Most importantly, a systematic procedure is provided for evaluating the performance of the proposed algorithm by estimating the distance of the obtained good enough solution from the optimal solution. The proposed algorithm is applied to a hotel booking limit (HBL) problem, which is a combinatorial stochastic simulation optimization problem. Extensive simulations are performed to demonstrate the computational efficiency of the proposed algorithm and the systematic performance evaluation procedure is applied to the HBL problem to quantify the goodness of the obtained good enough solution.