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Education
Ph.D., University of Waterloo, 1987
Professional Experience
Member, editorial board, Journal of Evolutionary Computation, 1992-present
Research Interests
There has been a
remarkable increase in
understanding of natural
adaptive systems in the last few years in areas like
molecular biology, immunology, embryology,
neuroscience, ecology, cognitive science, paleontology,
economics, and evolution. These have important
implications for artificial intelligence. In my view the
main task of artificial intelligence is to produce an
intelligence in the laboratory that can learn. Our largest
computing problems are too complex and poorly
understood for us to have any hope of simply programming
solutions to them as we did in the past.
My current work is in genetic algorithms, a branch of
machine learning, which is a branch of artificial intelligence.
My work focuses on the theoretical and engineering
consequences of various implementations of genetic
algorithms. So far my work has been restricted to proving
theoretical bounds of genetic algorithm performance, and on
extending the basic algorithm to more complex genetic
algorithms. My future work will focus on describing just
what mathematical properties of search spaces a genetic
algorithm exploits during its search.
Representative Publications
Rawlins, G. J. E., (1997).
Slaves of the Machine. MIT Press.
Rawlins, G. J. E., (1996).
Moths to the Flame. MIT Press.
Rawlins, G. J. E. & Louis, S. (1993). Why genetic algorithms?
Proceedings of the Fifth Midwest Artificial Intelligence and Cognitive Science Society Conference, T. E. Ahlswede (Ed.), MAICSS, 1-5.
Rawlins, G. J. E. & Louis, S. (1993). Syntactic analysis of convergence in genetic algorithms.
In D. Whitely (Ed.), Foundations of Genetic Algorithms 2, pp.
141-151. Morgan Kaufman.
Rawlings, G. J. E. (1992).
Compared to What?: An Introduction to the Analysis of Algorithms. Computer Science Press: W. H. Freeman.
Rawlins, G. J. E. (1991).
Foundations of Genetic Algorithms. Morgan Kaufmann.
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