Planning Genetic Algorithm: Pursuing Meta-knowledge
Date of Award
Doctor of Philosophy (PhD)
Graduate School of Computer and Information Sciences
Maxine S. Cohen
S. Rollins Guild
This study focuses on improving business planning by proposing a series of artificial intelligence techniques to facilitate the integration of decision support systems and expert system paradigms. The continued evolution of the national information infrastructure, open systems interconnectivity, and electronic data interchange lends toward the future plausibility of the inclusion of a back-end genetic algorithm approach. By using a back-end genetic algorithm, meta-planning knowledge could be collected, extended to external data sources, and utilized to improve business decision making.
Maury E. Johnson. 1999. Planning Genetic Algorithm: Pursuing Meta-knowledge. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, Graduate School of Computer and Information Sciences. (611)