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& Conclusions-A problem specific genetic algorithm (GA) is developed and demonstrated to analyze series-parallel systems and to determine the optimal design configuration when there are multiple component choices available for each of several k-out-of-n:G subsystems. The problem is to select components and levels of redundancy to optimize some objective function given system level constraints on reliability, cost and/or weight. Previous formulations of the problem have implicit restrictions concerning the type of redundancy allowed, the number of available component choices and whether mixing of components is allowed. The GA used to analyze this problem is a robust evolutionary optimization search technique with very few restrictions concerning the type or size of the design problem. The solution approach was to solve the dual of a nonlinear optimization problem by using a dynamic penalty function. The GA was demonstrated to perform very well on two types of problems, the redundancy allocation problem originally proposed by Fyffe, Hines and Lee and a second, randomly generated problem with more complex k-out-of-n:G configurations.

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