摘要： 针对遗传算法容易出现早熟的问题，提出一种基于k均值和量子遗传算法的径向基函数(RBF)神经网络组合优化方法。通过k均值聚类求取网络的中心，用量子遗传算法训练网络的权值，利用量子染色体的表示方式以及量子染色体的更新提高算法的并行性，从而解决遗传算法早熟的问题，提高网络的适应度。相对于PSO-RBF和ACO-RBF，该方法提高网络的收敛速度，实现对RBF网络的优化。
关键词:
早熟,
k均值,
量子遗传算法,
适应度
Abstract: According to the prematurity matter of the Genetic Algorithm(GA), this paper gives a complex optimization method based on k-means and Quantum Genetic Algorithm(QGA). It accounts the center of the RBF neural network through k-means, and trains the weights of RBF neural network by QGA. The express and renewal of quantum chromosome is used to improve the parallel of this procedure, so the problem of prematurity is solved, the fitness of network is improved, and convergence speed of the network is improved compared with the PSO-RBF and ACO-RBF. Optimization of the RBF neural network is implemented.
Key words:
prematurity,
k-means,
Quantum Genetic Algorithm(QGA),
fitness
中图分类号:
赵磊, 贾振红, 覃锡忠, 杨杰, 庞韶宁. 基于k均值和量子遗传算法的RBF网络优化[J]. 计算机工程, 2011, 37(10): 152-153.
DIAO Lei, GU Zhen-Gong, QIN Ti-Zhong, YANG Jie, LONG Shao-Ning. RBF Network Optimization Based on k-means and Quantum Genetic Algorithm[J]. Computer Engineering, 2011, 37(10): 152-153.