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Bloom Filter (BF) is a simple but powerful data structure that can check membership to a static set. The trade-off to use Bloom filter is a certain configurable risk of false positives. The odds of a false positive can be made very low if the hash bitmap is sufficiently large. Bin Bloom Filter (BBF) has number of BFs with different false positive rates based on their significance. Cuckoo Search (CS) is employed to assign different false positive rates to BFs which minimize the total membership invalidation cost. The experimental results have demonstrated for spam filtering using CS for various numbers of bins.