Advisor

Bowden, Royce O., Jr.

Committee Member

Greenwood, Allen G.

Committee Member

Bullington, Stanley F.

Committee Member

Jin, Mingzhou

Date of Degree

1-1-2003

Document Type

Graduate Thesis - Open Access

Major

Industrial Engineering

Degree Name

Master of Science

College

College of Engineering

Department

Department of Industrial Engineering

Abstract

With the rise in the application of evolution strategies for simulation optimization, a better understanding of how these algorithms are affected by the stochastic output produced by simulation models is needed. At very high levels of stochastic variance in the output, evolution strategies in their standard form experience difficulty locating the optimum. The degradation of the performance of evolution strategies in the presence of very high levels of variation can be attributed to the decrease in the proportion of correctly selected solutions as parents from which offspring solutions are generated. The proportion of solutions correctly selected as parents can be increased by conducting additional replications for each solution. However, experimental evaluation suggests that a very high proportion of correctly selected solutions as parents is not required. A proportion of correctly selected solutions of around 0.75 seems sufficient for evolution strategies to perform adequately. Integrating statistical techniques into the algorithm?s selection process does help evolution strategies cope with high levels of noise. There are four categories of techniques: statistical ranking and selection techniques, multiple comparison procedures, clustering techniques, and other techniques. Experimental comparison of indifference zone selection procedure by Dudewicz and Dalal (1975), sequential procedure by Kim and Nelson (2001), Tukey?s Procedure, clustering procedure by Calsinki and Corsten (1985), and Scheffe?s procedure (1985) under similar conditions suggests that the sequential ranking and selection procedure by Kim and Nelson (2001) helps evolution strategies cope with noise using the smallest number of replications. However, all of the techniques required a rather large number of replications, which suggests that better methods are needed. Experimental results also indicate that a statistical procedure is especially required during the later generations when solutions are spaced closely together in the search space (response surface).

URI

https://hdl.handle.net/11668/20099

Comments

Evolution strategies||Noise||Optimization||Simulation||Evolutionary Algorithms||Selection

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