In many real-world environments, a genetic algorithm designer is often faced with choosing the best fitness function from a range of possibilities. Fitness functions differ primarily based upon the speed, accuracy, and cost of a fitness evaluation. An important type of fitness function is the sampling fitness function, which utilizes sampling in order to reduce the noise of fitness evaluations. The accuracy and speed of a sampling fitness function are directly related to the sampel size, which is the number of sample size denotes the sample size that maximizes the performance of a genetic algorithm within a fixed time bound. In this paper, a domain independent lower bound of the optimal sample size is derived, and a sample size pruning method is described.
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