Ganzerli
GUCEA
The Center aims at advancing the knowledge of genetic algorithms (GA) in optimization problems. Optimization deals with seeking the best possible solution to a design problem. Often constraints are accounted for. The center was founded this year as a result of a 6-years collaborative effort between Professor De Palma in the Department of Mathematics and Computer Science and Dr. Sara Ganzerli, in the Department of Civil Engineering. Dr. Shannon Overbay joined us last year.
GA imitate the natural selection process. The capabilities of this method are very attractive in that they can handle discrete variables. GA are also able to solve problems with high epistasis that present interaction and coupling between different parameters of the function to be minimized. Applications include structural engineering and mathematics. In particular, genetic algorithms are applied to the optimal design of trusses considering uncertainties in the load condition and to graph theory. This ongoing study at Gonzaga University strives to design and implement genetic algorithms that generate reasonable solutions to difficult problems in mathematics, engineering, and science.
The broader impacts of the Center include:
Student participation. Training and support of students from Gonzaga University is focus of the Center. Funded students that have collaborated in research projects are:
Current Students Major Ryan Datteri Computer Science Ann Kilzer Computer Science/Mathematics/Art Katie Dahmen Computer Science/Mathematics Peter Stackle Computer Science
Past Students Major Sean Fitzgerald Computer Science Aaron Brown Computer Science Erica Hanson Civil Engineering Graduated (2004) Andrew Burton Computer Science Graduated (2004) Jared Smith Computer Science Graduated (2004) Matt Burkhart Computer Science Transferred to UW
All of the students were sponsored by the McDonald Award Program.
Teaching. A course on GA (CPSC 425 – Artificial intelligence IIa) was first taught during the 2002-2003 academic year and it will be continued in support of this research. The course benefits computer science students at large by being offered as a technical elective.
Continuing education. Continuing education seminars and invited lectures were scheduled targeting the structural engineering and computer science communities.
1. “Genetic algorithms as a polynomial time alternative for exponentially complex engineering and business problems” by De Palma, P., Ganzerli, S., Overbay, S., Brown, A., and Stackle, P., Inland Northwest chapter of ACM (Association for Computing Machinery), Spokane, WA. November 9, 2004.
2. “Genetic algorithms for optimal structural design considering convex models of uncertainty” Washington State University, Pullman, WA. April 24, 2003.
3. “Applications of genetic algorithms in Civil Engineering” by Sara Ganzerli and Paul DePalma. Inland Northwest chapter of ACM (Association for Computing Machinery), Spokane, WA. February 11, 2003.
Software development. We are using software of our design. The software can be adapted easily to different optimization problems.
Publications. This research has lead to several publications and presentations at international conferences that are the premier venue in the study of optimization, genetic algorithms, and uncertainties in structural mechanics.
1. Overbay, S., Ganzerli, S., De Palma, P., Stackle, P., Brown, A., “Trusses, NP-Completeness, and Genetic Algorithms.” 17th AC Specialty Conference, Saint Louis, MO, May 18-20, 2006. (Abstract accepted, full text submitted).
2. Ganzerli, S., De Palma, P., Stackle, P., Brown, A., “Info-gap uncertainty in structural optimization via genetic algorithms.” ICOSSAR’05, 9th International Conference on Structural Safety and Reliabilty, Rome, Italy, June 19-22, 2005, 2325-2330, Millpress Science Publishers, Rotterdam, NL.
3. Ganzerli, S., De Palma, P., Smith, J.D., and Burkhart, M.F., “Efficiency of genetic algorithms for optimal structural design considering convex models of uncertainty.” Proceedings of the Ninth International Conference on Statistics and Probability in Civil Engineering, Berkeley, CA, July 6-9, 2003, 1003-1010, Millpress Science Publishers, Rotterdam, NL.
4. Ganzerli, S., and Burkhart, M.F., “Genetic algorithms for optimal structural design using convex models of uncertainties.” Proceedings of the CSM4, Fourth International Conference on Computational Stochastic Mechanics, Kerkyra (Corfu), Greece. June 9-12, 2002, 207-214, Millpress Science Publishers, Rotterdam, NL.
Student Presentations.
1. Sean Fitzgerald (student, Gonzaga University) with: (Gonzaga students) Aaron Brown, Andrew Burton, and Peter Stackle (Gonzaga faculty) Paul DePalma (Mathematics and Computer Science), Sara Ganzerli (Engineering), and Shannon Overbay (Mathematics and Computer Science) "Natural Selection as a Means of Problem Solving." Annual Meeting of the Pacific Northwest Section of the Mathematical Association of America. University of Puget Sound, Tacoma, WA, April 1-2, 2005.
2. Burton, A., (De Palma, P., and Ganzerli, S.), “Truss optimization with genetic algorithms.” GECCO 2004, Genetic and Evolutionary Computation Conference, Seattle, WA, June 26-30, 2004.