Organized by the UVM Evolutionary Computation Group and Sponsored by:

DOE EPSCoR

and

The Departments of Plant Biology, Biology, Computer Science, and Civil & Environmental Engineering
 

Interdisciplinary Workshop

in Evolutionary Computing

Monday April 7, 2003

WORKSHOP SCHEDULE:

427 WATERMAN

10:00 Keynote Address – Dave Goldberg

              (Abstract)             (Biography)

11:00 Coffee Break

11:30 Tutorial – Dave Goldberg

501 WATERMAN

12:30 Luncheon – Grace Coolidge Room

413 WATERMAN

SHORT TALKS/DISCUSSION

2:30 Codons in Evolutionary Computation – Maggie Eppstein (UVM Computer Science)

3:15 Discovering Limits to Local Adaptation through Genetic Algorithms – Everett Weber, (UVM Biology)

3:45 Evolutionary Computation and Information Theory: An Effective Combination for Simultaneous Model Selection and Fitting – Jim Hoffmann (UVM Plant Biology)

4:15 An Industrial GA: Seeking Balance Between Premature Convergence and Unacceptable Run Time – Joe Watts (IBM Technology Development)

4:45 Solving Groundwater Inverse Problems using Genetic Algorithms – Donna Rizzo (UVM Environmental and Civil Engineering)

NOTE: The workshop and Luncheon are free but registration is required. Please email James.Hoffmann@uvm.edu or call Jim Hoffmann at 802-656-0429.
 

Featuring a Keynote Address by:

Dr. David E. Goldberg

University of Illinois

Urbana-Champaign

 Professor and Director
Illinois Genetic Algorithms Laboratory (http://www-illigal.ge.uiuc.edu)

Prospects for a Golden Age of Computational Innovation:
How Competent, Efficient Genetic Algorithms Will Change Our Future

Keynote Address

10:00 to 11:00am

427 Waterman Building

Maps, directions, and parking information: http://www.uvm.edu/map/

 Biography

David E. Goldberg (BSE, 1975, MSE, 1976, PhD, 1983, Civil Engineering, University of Michigan, Ann Arbor) is Professor of General Engineering at the University of Illinois at Urbana-Champaign (UIUC) and director of the Illinois Genetic Algorithms Laboratory (IlliGAL, http://www-illigal.ge.uiuc.edu/).  Between 1976 to 1980 he held a number of positions at Stoner Associates of Carlisle, PA, including Project Engineer and Marketing Manager.  Following his doctoral studies he joined the Engineering Mechanics faculty at the University of Alabama, Tuscaloosa, in 1984 and he moved to the University of Illinois in 1990.  Professor Goldberg was a 1985 recipient of a U.S. National Science Foundation Presidential Young Investigator Award, and in 1995 he was named an Associate of the Center for Advanced Study at UIUC.  He is founding chairman of the International Society for Genetic and Evolutionary Computation (http://www.isgec.org/), and his book Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, 1989) is widely used and cited.  His research focuses on the design, analysis, and application of genetic algorithms-computer procedures based on the mechanics of natural genetics and selection-and other innovating machines.  He has just completed a new book, The Design of Innovation: Lessons from and for Competent Genetic Algorithms (http://www-doi.ge.uiuc.edu/), that shows (1) how to design scalable genetic algorithms and (2) how such procedures are similar to certain processes of human innovation.

 Keynote Address Abstract

This talk suggests that well designed genetic algorithms (GAs)--so called competent GAs--are the computational embodiment of a number of the processes of human innovation.  It further examines five forces that are pushing the use of genetic algorithms into everyday practice and asks whether these forces are sustainable and where these forces will lead.  The talk answers that the forces are sustainable, and that we are now poised on the edge of a golden age of computational innovation.  Just as steam power gave humankind a kind of mechanical leverage that greatly amplified the capability of an individual during the industrial revolution, so too will genetic algorithms and other forms of computational innovation, provide us with a kind of innovation leverage during the golden age that will vastly multiply our ability to solve difficult problems quickly, reliably, accurately, and well.