Noise, Sampling, and Efficient Genetic Algorithms

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Miller, Brad L.

Contributors dc:contributor

• Goldberg, David E.

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI9737200

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81884