Matching Hyper-heuristics and Genetic Programming
Department
Computer Science
Major
Computer Science
Research Advisor
Tauritz, Daniel R.
Advisor's Department
Computer Science
Funding Source
OURE; Research contract from Los Alamos National Laboratory
Abstract
Modern society is faced with ever more complex problems, many of which can be formulated as generate-and-test optimization problems. General-purpose optimization algorithms are not well suited for real-world scenarios where many instances of the same problem class need to be repeatedly and efficiently solved, such as routing vehicles over highways with constantly changing traffic flows, because they are not targeted to a particular scenario. Hyper-heuristics automate the design of algorithms to create a custom algorithm for a particular scenario.
Hyper-heuristics typically employ Genetic Programming (GP) and this project has investigated the relationship between the choice of GP and performance in Hyper-heuristics. Results are presented demonstrating the existence of problems for which there is a statistically significant performance differential between the use of different types of GP. Also, some preliminary findings are presented on how to match the type of GP employed in a hyper-heuristic with the problem being addressed.
Biography
Sean is currently a senior in both Computer Science and Applied Mathematics. He is an Undergraduate Research Assistant in the Natural Computation Laboratory. He will be starting his Ph.D. in Computer Science at S&T effective Fall Semester 2015.
Research Category
Sciences
Presentation Type
Poster Presentation
Document Type
Poster
Location
Upper Atrium/Hall
Presentation Date
15 Apr 2015, 9:00 am - 11:45 am
Matching Hyper-heuristics and Genetic Programming
Upper Atrium/Hall
Modern society is faced with ever more complex problems, many of which can be formulated as generate-and-test optimization problems. General-purpose optimization algorithms are not well suited for real-world scenarios where many instances of the same problem class need to be repeatedly and efficiently solved, such as routing vehicles over highways with constantly changing traffic flows, because they are not targeted to a particular scenario. Hyper-heuristics automate the design of algorithms to create a custom algorithm for a particular scenario.
Hyper-heuristics typically employ Genetic Programming (GP) and this project has investigated the relationship between the choice of GP and performance in Hyper-heuristics. Results are presented demonstrating the existence of problems for which there is a statistically significant performance differential between the use of different types of GP. Also, some preliminary findings are presented on how to match the type of GP employed in a hyper-heuristic with the problem being addressed.
Comments
Joint project with Travis Bueter