Document Type

Thesis

Date of Award

8-31-1990

Degree Name

Master of Science in Electrical Engineering - (M.S.)

Department

Electrical and Computer Engineering

First Advisor

Irving Y. Wang

Second Advisor

Edwin Hou

Third Advisor

MengChu Zhou

Abstract

In this paper, a Genetic Algorithm technique is adapted to decompose the state transition lattice of a class of non-product form queueing models. Genetic Algorithms are search algorithms based upon the mechanics of natural genetics. They combine a survival-of-the-fittest among string structures with a structured, yet randomized, information exchange to form a search algorithm with some of the innovative flair of human search. While randomized, genetic algorithms are no simple random walk. They efficiently exploit historical information to speculate on new search points with improved performance. Here genetic algorithms is applied to a non-product queueing lattices optimization problem. Only the lattice of type A structure are considered. By applying this technique, the lattice is decomposed into solvable subsets which can be solved sequentially and independently.

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