Comparison of Genetic Algorithms with Conjugate Gradient Methods, Volume 2093
National Aeronautics and Space Administration, 1972 - Algorithms - 43 pages
Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.
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adaptive control associated inversion pattern average behavior best function value best string better string chosen randomly Conjugate Gradient Algorithms conjugate gradient methods control systems convergence rates crossover and inversion direct search algorithms employed in natural fixed step Fletcher-Reeves method four strings four subpopulations function evaluations required function evaluations taken function optimization problems Function Value Attained function value level genetic algorithms genetic methods gradient mutation highest function value history vector Index Squared indicated integer kind of cross-over last adaptation methods of mutation mutation method mutation routine Newton Raphson number of function number of strings Optimal Control pivot points probability vector Rastrigin rate of convergence Repeated Peaks replaced by string replaced the worst required by Version reset interval Schumer's method search strategies employed second level adaptation Spherical Contours standard deviation strings were chosen Test Function uniform random utility vector Value Function Attained version II system Version IV Version IVs Wood function