Face recognition using principal component analysis (PCA) and linear discriminant analysis (LDA) suffer from the loss of accuracy when the number of classes becomes large. This paper presents an effective genetic-based clustering algorithm (GCA) to preprocess a facial database into a two-layer database. Then, face recognition is done to minimize the similarity criterion in a specific cluster as in the traditional PCA-and LDA-based face recognition algorithms. Different from K-means clustering, the proposed GCA introduces a novel distance and a balance factor. The distance is defined to measure the similarity effectively between a class and the centroid of each cluster, and the balance factor is designed to achieve balanced clustering results. Experimental results on the Yale-B database in ideal and noisy conditions indicate that the proposed preprocessing method improves the recognition accuracy of the subspace recognition algorithms compared with K-means clustering. The proposed preprocessing method is also applicable to other recognition algorithms.
Masbiole (Multiagent Systems with Symbiotic Learning and Evolution) is a new methodology in conventional Multiagent Systems (MASs). In Masbiole, agents evolve considering not only their own benefits and losses, but also the benefits and losses of opponent agents. As a result, Masbiole can escape from Nash Equilibria and obtain better performances than conventional MASs. On the other hand, a newly developed evolutionary computing technique called Genetic Network Programming (GNP) which has the directed graph-type gene structure can develop and design the required intelligence mechanism for agents. As a result, GNP is considered to be well-suited for optimization problems in agents of MASs. Therefore, in this study, a test bed negotiation model is proposed using the evolutionary method of Masbiole as well as the evolutionary method of GNP, with the aim to study the effectiveness and efficiency of Masbiole in dynamic problems. The results obtained by the symbiotic evolution of the Masbiole systems are compared with those obtained by the GNP evolution.
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