A Query Optimization Method Based on Improved Genetic Algorithm
A query optimization and genetic algorithm technology, applied in the fields of genetic law, calculation, genetic model, etc., can solve the problems of inaccurate search of local extreme values, prolong the search time, reduce the search accuracy, etc., so as to improve the query speed and shorten the search. time, the effect of improving search accuracy
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Embodiment 1
[0027] Embodiment 1: as Figure 1-3 As shown, a query optimization method based on an improved genetic algorithm, the steps of the query optimization method are as follows:
[0028] Step1. Initial parameter setting: set the number of iterations G, the selection probability pc, the mutation probability pm, and define a query strategy set D as a search space. The query strategy in D requires n steps to complete the query task;
[0029] Step2. Establish a query strategy cost evaluation model: If the query strategy requires n steps to complete, then there are n+1 nodes in the query strategy space, the cost between two nodes is d, and the cost function is:
[0030]
[0031] Among them, X={x 1 ,x 2 ,...x i … x n+1} is the solution of the cost function, and x 1 ,x 2 ,...x n+1 Different from each other; the collection of all solutions is called the solution space, which means the query strategy set D; the nodes in the model represent the query state, and traversing n+1 nodes...
Embodiment 2
[0043] Embodiment 2: as Figure 1-3 As shown, a query optimization method based on an improved genetic algorithm, the steps of the query optimization method are as follows:
[0044] Step1. Initial parameter setting: set the number of iterations G, the selection probability pc, the mutation probability pm, and define a query strategy set D as a search space. The query strategy in D requires n steps to complete the query task;
[0045] Step2. Establish a query strategy cost evaluation model: If the query strategy requires n steps to complete, then there are n+1 nodes in the query strategy space, the cost between two nodes is d, and the cost function is:
[0046]
[0047] Among them, X={x 1 ,x 2 ,...x i … x n+1} is the solution of the cost function, and x 1 ,x 2 ,···x n+1 Different from each other; the collection of all solutions is called the solution space, which means the query strategy set D; the nodes in the model represent the query state, and traversing n+1 nodes...
Embodiment 3
[0056] Embodiment 3: as Figure 1-3 As shown, a query optimization method based on an improved genetic algorithm, the steps of the query optimization method are as follows:
[0057] Step1, initial parameter setting. A query strategy set D is defined as a search space, and the query strategies in D need 9 steps to complete the query task.
[0058] Step1.1. Determine initial parameters: number of iterations G=100, selection probability pc=0.2, mutation probability pm=0.4.
[0059] Step2. Establish a query strategy cost evaluation model. The time consumed by a certain query strategy to obtain the query result is the cost of this query strategy, and the function to calculate the cost of the query strategy is the cost function. A solution of the cost function provides a query strategy, and the query strategy set D is the solution space of the cost function.
[0060] Assuming that the query strategy requires n=9 steps to complete, then there are 10 nodes in the query strategy sp...
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