Degree Type

Thesis

Date of Award

1-1-2003

Degree Name

Master of Science

Department

Mechanical Engineering

Major

Mechanical Engineering

Abstract

Graph based evolutionary algorithms (GBEAs) have been used to control the rate of information flow and increase diversity in evolutionary algorithms, but there is limited understanding of the mechanics of this spread of information. This thesis proposes a metric, k[greek letter kappa], to quantify the rate at which information spread across an evolving graph based population. This metric provides a correlation between population size, graph type, and time for a good solution to spread across the entire graph. k[greek letter kappa] is roughly equivalent to the graph diameter for the graphs study. This metric can be used to compare population structures and sizes. This metric is then used to investigate the impact of the population size on the performance of GBEAs. As population size grows a trade off exists between preserving diversity and increased information spread. In most of the problems studied, there was an critical population size. In populations smaller than the critical population increased diversity, e.g. more disconnected graph based populations, resulted in faster solutions. In evolving populations larger than the critical population increased rate of information spread, e.g. more tightly connected graph based populations, resulted in faster solutions. Using these observations provides an initial basis for developing a method for determining a priori which graph would best preserve diversity, allowing for more difficult problems to be solved and/or producing multiple solutions to engineering design problems.

DOI

https://doi.org/10.31274/rtd-20200803-157

Copyright Owner

Steven Michael Corns

Language

en

OCLC Number

54618003

File Format

application/pdf

File Size

109 pages

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