Application on Query of Distributed Database Based on Improved Genetic Algorithm

Abstract:

The query cost usually as an important criterion for a distributed database. The genetic algorithm is an adaptive probabilistic search algorithm, but the crossover and mutation probability usually keep a probability in traditional genetic algorithm. If the crossover probability keep a large value, the possibility of damage for genetic algorithm model is greater; In turn, if the crossover probability keep a small value, the search process will transform a slow processing or even stagnating. If the mutation probability keep a small value, a new individual can be reproduced difficultly; In turn, if the mutation probability keep a large value, the genetic algorithm will as a Pure random search algorithm. To solve this problem, proposed a improved genetic algorithm that multiple possibility of crossover and mutation based on k-means clustering algorithm. The experiment results indicate that the algorithm is effective.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 4617-4621
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.4617
• Online since: May 2014
• Authors: Fu Xing Chen*, Xu Sheng Xie
• Keywords: Distributed Database, Distributed Query Optimization, Genetic Algorithm (GA), K-Means Clustering Algorithm

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