Genetic Algorithms and Genetic Programming for Multiscale Modeling: Applications in Materials Science and Chemistry and Advances in Scalability
Sastry, Kumara Narasimha
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https://hdl.handle.net/2142/87085
Description
Title
Genetic Algorithms and Genetic Programming for Multiscale Modeling: Applications in Materials Science and Chemistry and Advances in Scalability
Author(s)
Sastry, Kumara Narasimha
Issue Date
2007
Doctoral Committee Chair(s)
Goldberg, David E.
Johnson, Duane D.
Department of Study
Systems and Entrepreneurial Engineering
Discipline
Systems and Entrepreneurial Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Materials Science
Language
eng
Abstract
Finally, facetwise models are developed to explore limitations of scalability of MOGAs, where the scalability of multiobjective algorithms in reliably maintaining Pareto-optimal solutions is addressed. The results show that even when the building blocks are accurately identified, massive multimodality of the search problems can easily overwhelm the nicher (diversity preserving operator) and lead to exponential scale-up. Facetwise models are developed, which incorporate the combined effects of model accuracy, decision making, and sub-structure supply, as well as the effect of niching on the population sizing, to predict a limit on the growth rate of a maximum number of sub-structures that can compete in the two objectives to circumvent the failure of the niching method. The results show that if the number of competing building blocks between multiple objectives is less than the proposed limit, multiobjective GAs scale-up polynomially with the problem size on boundedly-difficult problems.
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