Abstract
This paper optimizes a well known NP-hard combinatorial problem—redundancy allocation—using a combined neural network and genetic algorithm (GA) approach. The GA searches for the minimum cost solution by selecting the appropriate components for a series-parallel system, given a minimum system reliability constraint. A neural network is used to estimate the system reliability value during search. This approach is an example of a computationally efficient method to apply GA optimization to problems for which repeated calculation of the objective function is impractical.
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