Created by W.Langdon from gp-bibliography.bib Revision:1.7428
An extensive empirical evaluation of a number of MLC NAND Flash devices is completed, identifying features for use with GP, before expressions are evolved to make predictions and estimations regarding the retention and endurance of blocks. The empirical evaluation highlights the large variations in performance between blocks in different devices of the same specification, and even between blocks within the same device. As well as building a data set for later use with GP, the durations of program and erase operations are identified as features with which to make endurance predictions and estimations, while a relationship between block location and endurance is also established.
GP is employed to evolve binary classification expressions, referred to as retention period classifiers, to predict whether blocks will correctly retain their data for a specified length of time. Following this, endurance classifiers are evolved to predict whether blocks will successfully complete a predefined number of cycles. Finally, symbolic regression expressions are evolved, building on the earlier experiments, to estimate the actual number of cycles each block will complete prior to failure and are referred to as endurance estimators.",
Genetic Programming entries for Damien Hogan