Microarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes but only a few hundreds of samples or less. Such extreme asymmetry between the dimensionality of genes and samples can lead to inaccurate diagnosis of disease in clinic. Therefore, it has been shown that selecting a small set of marker genes can lead to improved classification accuracy. In this paper, a simple modified ant colony optimization (ACO) algorithm is proposed to select tumor-related marker genes, and support vector machine (SVM) is used as classifier to evaluate the performance of the extracted gene subset. Experimental results on several benchmark tumor microarray datasets showed that the proposed approach produces better recognition with fewer marker genes than many other methods. It has been demonstrated that the modified ACO is a useful tool for selecting marker genes and mining high dimension data.

Abstract. Multi-agent reinforcement learning for multi-robot systems is a challenging issue in both robotics and artificial intelligence. But multi-agent reinforcement learning is bedeviled by the curse of dimensionality. In this paper, a novel hierarchical reinforcement learning approach named MOMQ is presented for multi-robot cooperation. The performance of MOMQ is demonstrated in three-robot trash collection task.

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With proper structure, one-dimensional (1D) binary nonperiodic dielectric multilayers can own compact strucuture and provide widely extended stop band from visible to infrared which is insensitive to the angle of incidence. The striking phenomenon attributes to the combining effect of Bragg reflection and light localization induced by disorder. The characteristic parameters to describe 1D binary nonperiodic dielectric multilayers are well defined. Material parameters of MgF2 and GaP are used to model 1D realistic device. Optimal design is suggested by comparison to binary λ0/4 stack reflectors with the same optical length, number of layers and degree of disorder.

A new approach for image segmentation based on visual attention mechanism is proposed. Motivated biologically, this approach simulates the bottom-up human visual selective attention mechanism, extracts early vision features of the image and constructs the saliency map. Multiple image features such as intensity, color and orientation in multiple scales are extracted to get some feature maps. The phase spectra of the feature maps are analyzed in frequency spectrum domain. Then the corresponding feature saliency maps are constructed in spatial domain and theses feature saliency maps are combined to an integrated saliency map.

<span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;; mso-fareast-font-family: 宋体; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">Salient region detection in images is very useful </span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;; mso-fareast-font-family: 宋体; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">for image processing applications like image compressing, image segmentation, object detection and recognition. In this paper, an improved approach to detect salient region is presented</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;; mso-fareast-font-family: 宋体; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">. </span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;; mso-fareast-font-family: 宋体; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">The proposed method can generate a robust saliency map and extract salient regions with precise boundaries. In the proposed method, local saliency, global saliency and rarity saliency of three kinds of low-level feature contrast of intensity, color and orientation are used to compute the visual saliency. A new feature integration strategy is proposed in this paper. This method can select features and compute the weights of the features dynamically by analyzing the effect of different features on the saliency. Then a more robust saliency map is obtained. It has been tested on many images to evaluate the validity and effectiveness of the proposed method. We also compare our method with other salient region detection methods and our method outperforms other methods in detection results.</span><span style="font-size: 10pt; font-family: &quot;Times New Roman&quot;; mso-fareast-font-family: 宋体; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-size: 9.0pt;" lang="EN-US"> </span>

Particle Swarm Optimization @SO) explores global optimal solution through exploiting the particle's memory and the swam's memory. Its properties of low constraint on the continuity of objective function and joint of search space, and ability of adapting to dynamic environment make PSO become one of the most important Swam Intelligence methods and Evolutionary Computation algorithms. The fundamental and standard algorithm is introduced firstly. Then the work OD the algorithm improvement during the past years is surveyed, as well as the applications on the mnlti-objective optimization, neural networks and electronics, etc. Finally, the problems remaining unresolved and some direetions of PSO research are discussed.

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