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https://hdl.handle.net/2142/81884
Description
Title
Noise, Sampling, and Efficient Genetic Algorithms
Author(s)
Miller, Brad L.
Issue Date
1997
Doctoral Committee Chair(s)
Goldberg, David E.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
Abstract
As genetic algorithms (GA) move into industry, a thorough understanding of how GAs are affected by noise is becoming increasingly important. Noise affects a GA's population sizing requirements, performance characteristics, and computational requirements. This research develops quantitative models for determining the effects of noise on the operation of a GA. Furthermore, the question of how to best optimize the performance of a GA in a noisy environments is investigated. Sampling fitness functions are explored, and techniques for determining the optimal sample size that maximizes the performance of a GA within a fixed computational time bound are presented.
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