High-Dimensional Approximate Nearest Neighbor Query Based on Fuzzy Control and Genetic Algorithm

Recently, with the constant development of application of high-dimensional data query, the index-based nearest neighbor retrieval has greatly improved the efficiency and accuracy of retrieval. The approximate nearest neighbor query is proposed and applied to get high performance to ease the curse of dimensionality to a certain extent. However, the heuristic pruning method may prune many local optimal solutions so that it is impossible to make sure to what extent the final results meet users´ needs. With the nearest neighbor as input, this paper proposes a method to get extent how the input meets user´s need based on fuzzy control, and then determines whether or not the input will be an approximate optimal solution with a comparison between the extent and threshold value. At the same time, the genetic algorithm is used to complete the adaptive study process of the threshold value to reflect users´ approximate needs.