Author(s)

Jeff Finckenor

Abstract

This study compares gradient solvers, Genetic Algorithms (GA's), and two kinds of GA- gradient hybrids. Gradient optimization methods can rapidly converge to an optimum solution. However, the solution is often a local optimum, particularly if the function is noisy. The local optimum may be far from the global optimum and depends on the user input starting point. Gradient solvers are also unable to perform integer optimization. GA's are effective at finding global solutions, but require many function evaluations. The GA is an integer optimizer and can lose resolution available to gradient optimizers when operating on a continuous function. The first hybrid uses the final GA solution as a starting point for the gradient solver. The second hybrid uses each GA individual as a s

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