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Network model recently becomes a popular tool for studying complex systems. Detecting meaningful communities in complex networks, as an important task in network modeling and analysis, has attracted great interests in various research areas. This paper proposes a genetic algorithm with a special encoding schema for community detection in complex networks. The algorithm employs a metric, named modularity Q as the fitness function and applies a special locus-based adjacency encoding schema to represent the community partitions. The encoding schema enables the algorithm to determine the number of communities adaptively and automatically, which provides great flexibility to the detection process. In addition, the schema also significantly reduces the search space. Extensive experiments demonstrate the effectiveness of the proposed algorithm.

A wide-field fluorescence microscope with a double-helix point spread function (PSF) is constructed to obtain the specimen's three-dimensional distribution with a single snapshot. Spiral-phase-based computer-generated holograms (CGHs) are adopted to make the depth-of-field of the microscope adjustable. The impact of system aberrations on the double-helix PSF at high numerical aperture is analyzed to reveal the necessity of the aberration correction. A modified cepstrum-based reconstruction scheme is promoted in accordance with properties of the new double-helix PSF. The extended depth-of-field images and the corresponding depth maps for both a simulated sample and a tilted section slice of bovine pulmonary artery endothelial (BPAE) cells are recovered, respectively, verifying that the depth-of-field is properly extended and the depth of the specimen can be estimated at a precision of 23.4nm. This three-dimensional fluorescence microscope with a framerate-rank time resolution is suitable for studying the fast developing process of thin and sparsely distributed micron-scale cells in extended depth-of-field.