Solving the redundancy allocation problem using a combined neural network/genetic algorithm approach

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.