Automatic starting point selection for function optimization

Traditional (non-stochastic) iterative methods for optimizing functions with multiple optima require a good procedure for selecting starting points. This paper illustrates how the selection of starting points can be made automatically by using a method based upon simulated annealing. We present a hybrid algorithm, possessing the accuracy of traditional routines, whilst incorporating the reliability of annealing methods, and illustrate its performance for a particularly complex practical problem.Now at the Statistical Laboratory, University of Cambridge, 16 Mill Lane, Cambridge, CB2 1SB, UK.