Evolutionary Search for Executable Solutions
Richard K. Belew
Computer Science & Engr. Dept.
Univ. California -- San Diego
Presented to CSE Faculty Proseminar, 26 Nov 97
The Genetic Algorithm (GA) is now widely used in a range of
contexts, from drug design to the inspection of silicon
wafers. In most current applications, the problem is posed
as one of "function optimization," where a very large, high-
dimensional space of is searched for the parameter values
which globally optimizes some objective function.
This talk will sketch some of the techniques as we attempt to
use the GA for an even more difficult search: finding
PROGRAMS. We know from standard results (like the Halting
and Busy Beaver problems) that many tests we might have for
programs are uncomputable. On the other hand, a number of
recent experiments have shown that some functional solutions
can be discovered by "genetic programming" techniques. As
time allows, I will survey three techniques explored by our
lab towards this goal: evolving neural networks; evolving
grammars; and coevolution.
Overheads used in the talk (Postscript, 242K, compressed)