Automated Generation of Benchmarks with High Discriminatory Power for Specific Sets of Black Box Search Algorithms
Department
Computer Science
Major
Computer Science
Research Advisor
Tauritz, Daniel R.
Advisor's Department
Computer Science
Funding Source
Missouri S&T Opportunities for Undergraduate Research Experiences (OURE) Program
Abstract
Black box search algorithms (BBSAs) vary widely in their effectiveness at solving particular classes of problems. It is paramount to be able to identify the most effective BBSA for a real-world problem. There exists a sizable set of standard benchmarks, but only for very narrow problem classes. Any such limited set is inherently unable to identify the general differences in effectiveness between particular BBSAs and practically infeasible for a sufficiently comprehensive comparison to differentiate between arbitrary BBSAs. Thus it is necessary to create custom benchmarks for given BBSAs to obtain benchmarks with high discriminatory power, utilizing a meta evolutionary algorithm approach in this case. By sampling the search space of a realworld problem and identifying the most similar custom benchmark, the previously identified BBSA with the best performance may be expected to be the most effective for solving that real-world problem. Results are presented showing the effectiveness of this approach.
Biography
Thomas is a junior in Computer Science at Missouri S&T. He is also pursuing minors in both bioinformatics and mathematics. He is an undergraduate research assistant in the Natural Computation Laboratory. His on-campus positions include Collegiate Eagle Scout Association president, Aerial Swing Dance Club instructor, and Student Council web administrator. In his free time, Thomas participates in many physical activities such as biking, Ultimate, and Frisbee golf. His future plans include graduate school and a career in computational science research.
Research Category
Sciences
Presentation Type
Poster Presentation
Document Type
Poster
Location
Upper Atrium/Hallway
Presentation Date
10 Apr 2012, 9:00 am - 11:45 am
Automated Generation of Benchmarks with High Discriminatory Power for Specific Sets of Black Box Search Algorithms
Upper Atrium/Hallway
Black box search algorithms (BBSAs) vary widely in their effectiveness at solving particular classes of problems. It is paramount to be able to identify the most effective BBSA for a real-world problem. There exists a sizable set of standard benchmarks, but only for very narrow problem classes. Any such limited set is inherently unable to identify the general differences in effectiveness between particular BBSAs and practically infeasible for a sufficiently comprehensive comparison to differentiate between arbitrary BBSAs. Thus it is necessary to create custom benchmarks for given BBSAs to obtain benchmarks with high discriminatory power, utilizing a meta evolutionary algorithm approach in this case. By sampling the search space of a realworld problem and identifying the most similar custom benchmark, the previously identified BBSA with the best performance may be expected to be the most effective for solving that real-world problem. Results are presented showing the effectiveness of this approach.
