GAS have proven effective on a broad range of search problems. However, when each population member's fitness evaluation is computationally expensive, the prospect of evaluating an entire population can prohibit use of the GA. This paper examines a GA that overcomes this difficulty by evaluating only a portion of the population. The remainder of the population has its fitness assigned by inheritance. Theoretical arguments justify this approach. An application to a GA-easy problem shows that greater efficiency can be obtained by evaluating only a small portion of the population. A real-world search problem confirms these results. Implications and future directions are discussed.

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