Solid State Drives (SSDs) have a number of significant advantages over traditional Hard Disk Drives (HDDs) but are currently far more expensive and have smaller capacities. These drives are based on NAND Flash memory devices, which have limited working lives. The number of times locations in such devices can be successfully programmed before they become unreliable is termed their endurance.There is currently no way to estimate accurately when a location within a Flash device will fail, so manufacturers give extremely conservative guarantees about the number of program operations their chips can endure. This paper describes a trial implementation of Genetic Programming (GP) used to evolve a Binary Classifier that predicts whether storage blocks within Flash memory devices will still be functioning correctly beyond some predefined number of cycles.The classifier is supplied with only the measured program and erase times from a relatively early point in the lifetime of a block. Using the relationships between these times, the system can accurately predict whether the block will continue to function satisfactorily up to a required number of cycles. Experiments on test sets comprised of unseen data show that our classifier obtains up to an average of 95% accuracy across 30 runs.

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