Highly Parallel Evolutionary Algorithms for Global Optimization, Symbolic Inference and Non-linear Regression

This work describes massively parallel genetic algorithms as a means for solving diicult global optimization, symbolic expression inference and curve tting problems. Results are shown for hard test cases and the implications for computational physics problems are discussed. The approach, eminently parallel, is believed to be competitive with more familiar methodologies used by computational scientists