Title

Automated Generation of Benchmarks with High Discriminatory Power for Specific Sets of Black Box Search Algorithms

Presenter Information

Thomas Reese

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

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Apr 10th, 9:00 AM Apr 10th, 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.