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ERIC Number: ED552078
Record Type: Non-Journal
Publication Date: 2012
Pages: 205
Abstractor: As Provided
Reference Count: N/A
ISBN: 978-1-2678-8178-6
ISSN: N/A
Memetic Algorithms, Domain Knowledge, and Financial Investing
Du, Jie
ProQuest LLC, Ph.D. Dissertation, University of Maryland, Baltimore County
While the question of how to use human knowledge to guide evolutionary search is long-recognized, much remains to be done to answer this question adequately. This dissertation aims to further answer this question by exploring the role of domain knowledge in evolutionary computation as applied to real-world, complex problems, such as financial investing. The hypothesis is that domain knowledge can be combined with evolutionary algorithms (EAs) so as to systematically influence the traversal of a search space. A framework for incorporating domain knowledge into memetic algorithms, a specific kind of EAs, is proposed and is empirically evaluated by experiments that focus on knowledge-guided pre-processing, representation/initialization, and reproduction. Knowledge can be incorporated into an EA adaptedly (e.g., pre-processing and initialization) or adaptively (e.g., representation and reproduction). From the "adapted" perspective, knowledge can facilitate data collection and data cleansing in the preprocessing phase. Knowledge can also be utilized to create the initial population. From the "adaptive" perspective, individuals are represented by a sequence of components (or building blocks) that are mappable to the nodes in a semantic net, on which mutation and crossover are conducted. The mutation changes components to ones based on a certain semantic distance or more dynamically, based on a function of semantic distance. The crossover swaps the building blocks between two fit individuals, where the crossover point is constrained to be the boundary of these building blocks. The test problems are asset valuation and portfolio optimization. The experimental results show that knowledge can systematically influence the traversal of a search space. More interestingly, the results show how conceptual distance in human knowledge can correspond to distance between evolutionary individuals. According to the problems being solved, knowledge might be dynamically used to increase or decrease the reproduction size in a search algorithm. These results shed light on the role of knowledge in EAs and are part of the larger body of work in the field of evolutionary computation to delineate how domain knowledge might usefully constrain EAs. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]
ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml
Publication Type: Dissertations/Theses - Doctoral Dissertations
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A