@inproceedings{96aab54783c4443cbcf0d1bbdf609648,
title = "Evolving a retention period classifier for use with flash memory",
abstract = "Flash memory based Solid State Drives (SSDs) are gaining momentum toward replacing traditional Hard Disk Drives (HDDs) in computers and are now also generating commercial interest from enterprise data storage companies. However, storage locations in Flash memory devices degrade through repeated programming and erasing. As the storage blocks within a Flash device deteriorate through use, their ability to retain data while powered off over long periods also diminishes. Currently there is no way to predict whether a block will successfully retain data for a specified period of time while powered off. We detail our use of Genetic Programming (GP) to evolve a binary classifier which predicts whether blocks within a Flash memory device will still satisfactorily retain data after prolonged use, saving considerable amounts of testing time. This is the first time a solution to this problem has been proposed and results show an average of over 85% correct classification on previously unseen data.",
keywords = "Binary Classifier, Flash Memory, Genetic Programming, Solid State Drive",
author = "Damien Hogan and Tom Arbuckle and Conor Ryan and Joe Sullivan",
year = "2012",
language = "English",
isbn = "9789898565334",
series = "IJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence",
pages = "24--33",
booktitle = "IJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence",
note = "4th International Joint Conference on Computational Intelligence, IJCCI 2012 ; Conference date: 05-10-2012 Through 07-10-2012",
}