Visualization for hyper-heuristics
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. While such automated design has great advantages, it can often be hard to apply to real-world problems and difficult to understand exactly how a design was derived and why it should be trusted. This project aims to address these issues of usability and understandability, by creating an easy-to-use graphical user interface for hyper-heuristics to support practitioners, as well as easy-to-understand scientific visualization of the produced automated designs for practitioners and researchers.
Biography
Lauren is currently a senior in Computer Science, an Undergraduate Research Assistant in the Natural Computation Laboratory, the Publicity Officer for the Missouri S&T Student Chapter of ACM SIG-Security, and the Webmaster for the Missouri S&T Student Chapter of ACM-W. She will be graduating from Missouri University of Science and Technology in May 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
Visualization for hyper-heuristics
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. While such automated design has great advantages, it can often be hard to apply to real-world problems and difficult to understand exactly how a design was derived and why it should be trusted. This project aims to address these issues of usability and understandability, by creating an easy-to-use graphical user interface for hyper-heuristics to support practitioners, as well as easy-to-understand scientific visualization of the produced automated designs for practitioners and researchers.
Comments
Joint project with Luke Simon