A Meta-Parallel Evolutionary System for Solving Optimization Problems
Metadata Field | Value | Language |
---|---|---|
dc.contributor.advisor | Dozier, Gerry | |
dc.contributor.advisor | Biaz, Saad | en_US |
dc.contributor.advisor | Hamilton, John A., Jr. | en_US |
dc.contributor.advisor | Lim, Alvin S. | en_US |
dc.contributor.author | Britt, Winard | en_US |
dc.date.accessioned | 2008-09-09T21:13:30Z | |
dc.date.available | 2008-09-09T21:13:30Z | |
dc.date.issued | 2007-05-15 | en_US |
dc.identifier.uri | http://hdl.handle.net/10415/71 | |
dc.description.abstract | The purpose of the Meta-Parallel Evolutionary System (MPES) is to develop fast, efficient parallel evolutionary systems for function optimization. Given an optimization problem and a set number of nodes available for the computation, the MPES searches for a strong, potentially heterogeneous combination of evolutionary algorithms to coordinate in order to effectively solve a problem. The Evolutionary Algorithms that are utilized in the parallel system are a Particle Swarm Optimizer (PSO), a variety of Genetic Algorithms (GAs), and an Evolutionary Hill-Climber Algorithm (EHC). The subpopulations communicate with each other via a centralized buffer. At a higher level exists the MPES, which uses evolutionary methods in order to discover parameters for effective parallel systems. This methodology provides an immediate benefit in the form of a strong tool to solve the optimization problem. Further, it provides a long-term benefit by identifying a system that has the potential to effectively solve other difficult optimization problems with a similar search space. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Computer Science and Software Engineering | en_US |
dc.title | A Meta-Parallel Evolutionary System for Solving Optimization Problems | en_US |
dc.type | Thesis | en_US |
dc.embargo.length | NO_RESTRICTION | en_US |
dc.embargo.status | NOT_EMBARGOED | en_US |