A review of some exchange algorithms for constructing discrete D-optimal designs
Computational Statistics & Data Analysis - Second special issue on optimization techniques in statistics
Accelerating the convergence of random search methods for discrete stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the 33nd conference on Winter simulation
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Simulation optimization methods that combine multiple comparisons and genetic algorithms with applications in design for computer and supersaturated experiments
Scheduling of genetic algorithms in a noisy environment
Evolutionary Computation
An open-source population indifference zone-based algorithm for simulation optimization
Proceedings of the Winter Simulation Conference
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The problem of finding the binomial population with the highest success probability is considered when the number of binomial populations is large. A new rigorous indifference zone subset selection procedure for binomial populations is proposed with the proof of the corresponding least favorable configuration. For cases involving large numbers of binomial populations, a simulation optimization method combining the proposed subset selection procedure with an elitist Genetic Algorithm (GA) is proposed to find the highest-mean solution. Convergence of the proposed GA frame work are established under general assumptions. The problem of deriving supersaturated screening designs is described and used to illustrate the application of all methods. Computational comparisons are also presented for the problem of generating supersaturated experimental designs.