The availability of physics-based modeling tools that represent phenomena accurately enables scientists and engineers to explore the physical world by simulations. As the computing power increases these new physics-based simulation tools will take the place of the simple analytical models that have been used for optimization, analysis, and synthesis problems in the past. Due to the increased scope of the tools the number and complexity of inputs to these tools is increased as well. Some of these parameters are simple, well known physical constants, others might bear unknown physical insight into the underlying problem. The exploration of this increasing design or parameter space is a formidable task that in many cases cannot be performed by standard search or gradient based methods.
GAs enable the global optimization in a given parameter space given a broad population diversity.
The evaluation of the fitness of one individual in the population is independent of the others and lends itself for perfectly scalable parallelization. Such parallel algorithms have been developed in the past. This prototype has been used to nanoelectronic structures, establish atomistic basis sets, and electronic circuits. Future efforts will enable the remote control of the supercomputer simulation in a true client server approach from the desk-top computer and the monitoring of the simulation throughout the generations.