[Colloquium] David E. Goldberg on Friday, October 24, 2003

[Margery Ishmael]

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DEPARTMENT OF COMPUTER SCIENCE - TALK

Friday, October 24, 2003 at 2:30 p.m. in Ryerson 251

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Speaker: David E. Goldberg, University of Illinois at Urbana-Champaign
http://www-illigal.ge.uiuc.edu/

Title: The Design of Innovation: Lessons from and for Competent Genetic  
Algorithms
 
Abstract:
One of the attractions of genetic algorithms–search procedures based on  
the mechanics of natural selection and genetics–is that the processes  
involved seem intuitively similar to those of human innovation, but  
that connection is not very helpful to genetic algorithm design,  
because the processes and dynamics of innovation are themselves not  
well understood. In fact, the more useful line of inquiry comes from  
reversing the chain of thought. As we learn more about the design of  
competent GAs—GAs that solve hard problems, quickly, reliably, and  
accurately—it appears that the dynamics and processes of innovation are  
succumbing to a more mechanistic understanding.

This talk examines the implications of the design of competent GAs for  
the understanding of cross-fertilizing (selectorecombinative)  
innovation processes. Starting from a brief introduction of the  
mechanics and effect of simple GAs and moving toward the sevenfold  
design decomposition that has been used to develop competent GAs since  
1993, a working, quantitative theory of innovation is constructed. The  
theory is predictive in that calculation of critical "time" and  
"length" scales can be combined with dimensional reasoning to determine  
important dimensionless ratios such as the innovation number that help  
us understand when and how well a innovative process will work and  
whether the scale to larger, more difficult situations. Scalability is  
demonstrated using examples from a number of competent GAs, including  
the fast messy GA (fmGA) and the hierarchical Bayesian optimization  
algorithm (hBOA). Although the detailed mechanisms of these procedures  
is strikingly different, the "physics" they follow is essentially the  
same. 

Speaker Biographical Sketch

David E. Goldberg (BSE, 1975, MSE, 1976, PhD, 1983 in Civil Engineering  
from the University of Michigan, Ann Arbor) is Jerry S. Dobrovolny  
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 was founding chairman of the International Society  
for Genetic and Evolutionary Computation (http://www.isgec.org/), and  
his first book Genetic Algorithms in Search, Optimization and Machine  
Learning (Addison-Wesley, 1989) is listed as the 4th most cited  
reference in computer science according to CITESEER. 

Host: Stuart A. Kurtz

*Refreshments will follow the talk in Ryerson 255*

People in need of assistance should call 773-834-8977 in advance.


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