关键词: 遗传算法, 自组织映射, 聚类分析, 超启发搜索, 图像分割

Abstract:  Genetic clustering algorithm can obtain the optimal solution on the condition of the larger population, however, which leads to a slow convergence speed. In order to tackle the challenge problem, this paper proposes a novel hyper-heuristic genetic cluster algorithm based on self-organizing map. Firstly, the data space is converted to feature space by exploiting self-organizing map method. Secondly, one solution can be found by employing genetic algorithm in the feature space to diminish the computational load of the presented algorithm. Then， the one solution can be reflected to the data space. Moreover, another solution can be found by the K-means algorithm in the data space. The optimal solution is obtained according to the optimal method, which the same cluster results maintained and the different ones are clustered again to further ensure optimal solution. The extensive simulation results demonstrate the proposed algorithm has much higher accurate rate in the case of small population in comparison with genetic clustering algorithm.

Key words: genetic algorithm, self-organizing map, cluster analysis, hyper-heuristics search, image segmentation