Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Using Genetic Algorithms with Small Populations
Proceedings of the 5th International Conference on Genetic Algorithms
VLSID '00 Proceedings of the 13th International Conference on VLSI Design
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This paper describes the application of evolutionary search to the problem of Flash memory wear-out. The operating parameters of Flash memory are notoriously difficult to determine, as the optimal values vary from batch to batch. These parameters are usually established by an expensive, once off process of manual destructive testing at design time. Testing on individual batches is normally not feasible. We establish the viability of a platform that performs destructive experimentation on hard silicon, using a Genetic Algorithm to automatically discover optimal operating parameter settings. The results demonstrate a minimum average life extension of between 250% and 350% over the factory set read write and erase conditions with a maximum life extension exhibited of 700% for cells within the same device. It was necessary to build specialized hardware to perform the repetitive testing required by the GA, here we describe this hardware and demonstrate how the lessons learned in this pilot study will allow us to proceed with a more complex parallel evaluation platform, which will facilitate a larger problem space, larger population size and diversity of search techniques, facilitating the near no cost life extension of a split-gate Flash memory device.