Genetic Algorithms and Genetic Programming for Multiscale Modeling: Applications in Materials Science and Chemistry and Advances in Scalability

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Systems and Entrepreneurial Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Sastry, Kumara Narasimha

Contributors dc:contributor

• Goldberg, David E.
• Johnson, Duane D.

• Engineering, Materials Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI3290369

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/87085