An evolutionary network driven by dynamics is studied and applied to the graph coloring problem.From an initial structure,both the topology and the coupling weights evolve according to the dynamics.On the other hand,the dynamics of the network are determined by the topology and the coupling weights,so an interesting structure-dynamics co-evolutionary scheme appears.By providing two evolutionary strategies,a network described by the complement of a graph will evolve into several clusters of nodes according to their dynamics.The nodes in each cluster can be assigned the same color and nodes in different clusters assigned different colors.In this way,a co-evolution phenomenon is applied to the graph coloring problem.The proposed scheme is tested on several benchmark graphs for graph coloring.
Purpose–The purpose of this paper is to present a Differential Immune Clone Clustering Algorithm(DICCA)to solve image segmentation.Design/methodology/approach–DICCA combines immune clone selection and differential evolution,and two populations are used in the evolutionary process.Clone reproduction and selection,differential mutation,crossover and selection are adopted to evolve two populations,which can increase population diversity and avoid local optimum.After extracting the texture features of an image and encoding them with real numbers,DICCA is used to partition these features,and the final segmentation result is obtained.Findings–This approach is applied to segment all sorts of images into homogeneous regions,including artificial synthetic texture images,natural images and remote sensing images,and the experimental results show the effectiveness of the proposed algorithm.Originality/value–The method presented in this paper represents a new approach to solving clustering problems.The novel method applies the idea two populations are used in the evolutionary process.The proposed clustering algorithm is shown to be effective in solving image segmentation.
