  Genetic algorithms are a class of heuristic search techniques that apply
basic evolutionary operators in a computational setting. We have designed a
fully parallel and distributed hardware/software implementation of the
generalized optimization subroutine PIKAIA, which utilizes a genetic algorithm
to provide an objective determination of the globally optimal parameters for a
given model against an observational data set. We have used this modeling tool
in the context of white dwarf asteroseismology, i.e., the art and science of
extracting physical and structural information about these stars from
observations of their oscillation frequencies. The efficient, parallel
exploration of parameter-space made possible by genetic-algorithm-based
numerical optimization led us to a number of interesting physical results: (1)
resolution of a hitherto puzzling discrepancy between stellar evolution models
and prior asteroseismic inferences of the surface helium layer mass for a DBV
white dwarf; (2) precise determination of the central oxygen mass fraction in a
white dwarf star; and (3) a preliminary estimate of the astrophysically
important but experimentally uncertain rate for the ^12C(alpha,gamma)^16O
nuclear reaction. These successes suggest that a broad class of
computationally-intensive modeling applications could also benefit from this
approach.
