Minds at UW (University of Wisconsin) · 1991 · 116 citations · 5 references
Open access
ComputingComputer ScienceData StructuresDatabasesEvolutionary DesignCrossover OperatorsEvolutionary ComputationEvolutionary OptimizationGenetic AlgorithmsMutation OperatorsQuery OptimizationBinary Search TreesBinary TreesGenetic Algorithm OptimizationRepresentation In Evolutionary AlgorithmsTree Crossover
Current query optimization techniques are inadequate to support some of the emerging database applications. In this paper, we outline a database query optimization problem and describe the adaptation of a genetic algorithm to the problem. We present a method for encoding arbitrary binary trees as chromosomes and describe several crossover operators for such chromosomes. Preliminary computational comparisons with the current best--known method for query optimization indicate this to be a promising approach. In particular, the output quality and the time needed to produce such solutions is comparable to and in general better than the current method. 1 INTRODUCTION Genetic algorithms [4, 6] are becoming a widely used and accepted method for very difficult optimization problems. In this paper, we describe the implementation of a genetic algorithm (GA) for a problem in database query optimization. In order to give a careful formulation of our GA, we first give a broad outline of this parti...
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