Theses and Dissertations at Montana State University (MSU)
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Item An empirical study of parallel genetic algorithms for the traveling salesman and job-shop scheduling problems(Montana State University - Bozeman, College of Engineering, 2001) Helzer, David MichaelItem Two heuristic stochastic algorithms applied to a temporally constrained scheduling program(Montana State University - Bozeman, College of Engineering, 2001) Furois, Scott AlanItem An enumerative approach to computing cut sets in metabolic networks(Montana State University - Bozeman, College of Engineering, 2013) Salinas, Daniel; Chairperson, Graduate Committee: Brendan MumeyThe productivity of organisms used in biotechnology may be enhanced when certain parts of their metabolism are rendered inaccessible. This can be achieved with genetic modifications, but current techniques set a practical limit on number of modifications that can be applied. Taking advantage of this limit, we implement a brute force algorithm that can compute cut sets for any set of metabolites and reactions that is shown to perform better than alternative approaches. Also, an attempt is made to approximate a binary linear program with a quadratic program; this approximation is meant to be used when refining the growth model of organisms used in flux balance analysis. The approximation is shown to be less efficient that the original program. Finally, extensions to the brute force algorithm are proposed.Item On mutation and crossover in the theory of evolutionary algorithms(Montana State University - Bozeman, College of Engineering, 2010) Richter, James Neal; Chairperson, Graduate Committee: John Paxton; Alden Wright (co-chair)The Evolutionary Algorithm is a population-based metaheuristic optimization algorithm. The EA employs mutation, crossover and selection operators inspired by biological evolution. It is commonly applied to find exact or approximate solutions to combinatorial search and optimization problems. This dissertation describes a series of theoretical and experimental studies on a variety of evolutionary algorithms and models of those algorithms. The effects of the crossover and mutation operators are analyzed. Multiple examples of deceptive fitness functions are given where the crossover operator is shown or proven to be detrimental to the speedy optimization of a function. While other research monographs have shown the benefits of crossover on various fitness functions, this is one of the few (or only) doing the inverse. A background literature review is given of both population genetics and evolutionary computation with a focus on results and opinions on the relative merits of crossover and mutation. Next, a family of new fitness functions is introduced and proven to be difficult for crossover to optimize. This is followed by the construction and evaluation of executable theoretical models of EAs in order to explore the effects of parameterized mutation and crossover. These models link the EA to the Metropolis-Hastings algorithm. Dynamical systems analysis is performed on models of EAs to explore their attributes and fixed points. Additional crossover deceptive functions are shown and analyzed to examine the movement of fixed points under changing parameters. Finally, a set of online adaptive parameter experiments with common fitness functions is presented.