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Doane Undergraduate Genetic Algorithms Lab (DUGAL)

 Welcome to DUGAL
Welcome to the home page for Dr. Mark Meysenburg's computer science research group, the Doane Undergraduate Genetic Algorithms Lab (DUGAL)! The group is dedicated to exploring the application of evolutionary computation in general, and genetic algorithms specifically, to solve a wide variety of interdisciplinary problems. DUGAL emphasizes research by undergraduate students in these areas.

Evolutionary Computation and Genetic Algorithms
Evolutionary computation (EC) attempts to solve complex, vexing problems through a simulation of natural evolutionary processes. In our group, we most often utilize a specific EC technique, genetic algorithms (GAs). In a GA, we randomly create a population of individuals, who all represent potential solutions to the problem at hand. Over time, these individuals mate with each other, produce offspring, undergo mutation, and are subjected to a "survival of the fittest" selection process. Over many generations, individuals emerge in the population that are acceptable solutions to the problem being studied.

The Evolutionary Computation page goes into more depth regarding EC techniques, and also provides external links to various EC resources.

The Doane Evolutionary Algorithm (DEA)
The Doane Evolutionary Algorithm (DEA) is a Java-coded EC framework that supports many approaches, including:

  • Fixed-length, binary chromosome individual genetic algorithms
  • Fixed-length, integer chromosome individual genetic algorithms
  • Fixed-length, floating point chromosome individual genetic algorithms

The DEA framework, described in more depth on the DEA page, has a simple architecture that makes it easily approachable by undergraduate research students, even those early in their university careers.

Doane Student Members
Since 1998, many students have completed projects in our group, as Evolution Computation class projects, IST 495 Senior Seminar projects, or dedicated undergraduate research projects. Here is a partial list of our student collaborators:

  • Emily Alfs, Computer Science major, class of 2016
  • Chris Craven, Information Systems major, class of 2012
  • Dan Hoelting, Computer Science major, class of 2004
  • Duane McElvain, Computer Science major, class of 2004
  • Jason Nelson, Computer Science major, class of 2003

DUGAL Projects and Papers
There are several ongoing projects in the DUGAL group that students can join. Examples include evolving pseudo-random number generators, a crowd-sourced Haiku creation system, the automatic evaluation of SuDoKu game difficulty, and more. But, the beauty of evolutionary computation is that the technique can be applied to a wide variety of problems in almost every academic discipline. If you have an idea for a project you'd like to try, contact Dr. Meysenburg!

Over the years, we've been successful in publishing our work. Our most recent papers are linked below, and a more complete list can be found on the Projects and Papers page.

  • [A16] Emily Alfs. Creating a Difficulty Metric for A Sudoku Variation. In MICS 2016: Proceedings of the Midwest Instruction and Computing Symposium, Cedar Falls, IA. Senior seminar project supervised by Mark M. Meysenburg.
  • [A15] Emily Alfs. Understanding 31-derful. In MICS 2015: Proceedings of the Midwest Instruction and Computing Symposium, Grand Forks, ND, April 2015. URL: http://www.micsymposium.org/mics2015/ProceedingsMICS_2015/Alfs_4C1_1.pdf, active as of 3/24/2016. Undergraduate research project supervised by Mark M. Meysenburg.
  • [C12] Chris Craven. Genetic Algorithms in College and University Housing. In MICS 2012: Proceedings of the Midwest Instruction and Computing Symposium, Cedar Falls, IA, April 2012. URL: http://micsymposium.org/mics2012/submissions/mics2012_submission_31.pdf, active as of 3/24/2016. Senior seminar project supervised by Mark M. Meysenburg.