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Title:Noise, Sampling, and Efficient Genetic Algorithms
Author(s):Miller, Brad L.
Doctoral Committee Chair(s):Goldberg, David E.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Computer Science
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.
Issue Date:1997
Description:157 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997.
Other Identifier(s):(MiAaPQ)AAI9737200
Date Available in IDEALS:2015-09-25
Date Deposited:1997

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