Predicting Weather Using a Genetic Memory: A Combination of Kanerva's Sparse Distributed Memory with Holland's Genetic Algorithms

Part of Advances in Neural Information Processing Systems 2 (NIPS 1989)

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David Rogers


Kanerva's sparse distributed memory (SDM) is an associative-memo(cid:173) ry model based on the mathematical properties of high-dimensional binary address spaces. Holland's genetic algorithms are a search tech(cid:173) nique for high-dimensional spaces inspired by evolutionary processes of DNA. "Genetic Memory" is a hybrid of the above two systems, in which the memory uses a genetic algorithm to dynamically recon(cid:173) figure its physical storage locations to reflect correlations between the stored addresses and data. For example, when presented with raw weather station data, the Genetic Memory discovers specific fea(cid:173) tures in the weather data which correlate well with upcoming rain, and reconfigures the memory to utilize this information effectively. This architecture is designed to maximize the ability of the system to scale-up to handle real-world problems.