Research And Application Of Quantum Evolutionary Algorithm And Mapreduce

21st century is an era of information, information and data grow rapidly, this is a higher demand on our computing power, cloud computing came into being in this environment, It has brought us a new change. Cloud computing is a commercial calculation model, in this model, the computing tasks are distributed in a pool of a large number of computer resources, and it makes all kinds of application system can acquire computing power, storage space and information services according to the need. Cloud computing is the further development of distributed computing, grid computing and parallel computing, it has given us a more efficient parallel model, so how to put the existing parallel algorithms used in cloud computing become the content of our study.Write parallel programs on the cloud platform, which is different from the traditional parallel procedures, traditional parallel realization is mainly based on multi-threaded, and limited in the single machine inside. Parallel thought on cloud platform focuses on multiple computers, even the computer clusters, and the construction of the cloud environment is based on the ordinary computer. The main idea is that dividing a big task into many small tasks which are carried out on computer clusters, which will greatly reduce the cost. The field of data mining is often plagued by huge amounts of data, if introduce the cloud computing to the field of data mining, is bound to bring a new change.MapReduce is put forward in2004by Google, it is a programming model for the mass data calculation under the large cluster, and it is mainly used to handle large amount of information and complete the task within an acceptable time. Now MapReduce model has been used to solve some problems in data mining and machine learning.In recent years, many researchers begin to pay more attention to quantum evolutionary algorithm, quantum evolutionary algorithm has natural parallelism, and suits the realization on large-scale parallel computer, and cloud platform for quantum evolutionary algorithm and the realization of the parallel established the material basis. Covering algorithm, a solution of classification problems in data mining algorithms, was originally proposed by Professor Zhang Ling, It is a constructive neural network learning algorithm, using the geometric meaning of the M-P neurons. The essence of Covering algorithm is that regard coverage areas as the hidden layer of the three-layer neural network, regard the test set as input layer, and regard the classification results of the test set as output layer. Currently Covering algorithm has been widely promoted.In this paper, taking advantage of the natural parallelism of the quantum evolutionary algorithm and the cloud platform, realized the parallel quantum evolutionary algorithm on cloud platform, and the results show that there will be a better parallel efficiency in a cloud platform. To further study the performance of quantum evolutionary algorithm, this paper takes advantages of it, for example: diversity features good, small population, fast convergence, strong global optimal performance and so on, introduces it into covering algorithm to optimize coverage center, adopts fitness to evaluate the pros and cons of the solution, and propose an improved quantum-optimal coverage algorithm. Through experiment and comparative analysis in five groups of data, its results show that the proposed algorithm can effectively improve the classification accuracy and efficiency. At last, using MapReduce model realized the processing and retrieval of taobao’s data information.