This instrument is now mature, and has provided a wealth of seismological data on the different varieties of pulsating white dwarf stars. In an effort to bring the analysis of WET data to the level of sophistication demanded by these observations, we have recently developed a new model-fitting method based on a genetic algorithm . The underlying ideas for genetic algorithms were inspired by Charles Darwin's notion of biological evolution through natural selection. The basic idea is to solve a problem by evolving the best solution from an initial set of random guesses. The computer model provides the framework within which the evolution takes place, and the individual parameters controlling it serve as the genetic building blocks.
Observations provide the selection pressure.
In practice, this method is much more efficient than other comparably global techniques. Although extremely effective and objective in their application, genetic algorithms still require a very large amount of computer time because they involve running thousands of complex models for each set of observations. The potential of this approach to probe interesting physics is clear. What we can accomplish by applying it to other classes of objects is limited only by the computational resources that we can devote to each problem. We are currently raising funds to build a new parallel computer based on our proven design, and to duplicate it at two collaborating institutions, so we can probe other classes of white dwarf stars using this new method.