In this paper, we propose a segmentation method based on region growing and evolutionary algorithms. Before segmentation, the number of classes is determined by the principle of maximum entropy. The proposed approach ...
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In this paper, we propose a segmentation method based on region growing and evolutionary algorithms. Before segmentation, the number of classes is determined by the principle of maximum entropy. The proposed approach is validated on some synthetic and real images and, it shows to be very interesting as decision support in quality control. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Conference Program Chairs.
In this paper, we proposed a new Pareto generic algorithm which hybridizes genetic algorithm and artificial immune systems. Numerical experiments were made using a classical benchmark in multiple-objective optimizatio...
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ISBN:
(纸本)9781424450534
In this paper, we proposed a new Pareto generic algorithm which hybridizes genetic algorithm and artificial immune systems. Numerical experiments were made using a classical benchmark in multiple-objective optimization (MOKP). Results show that our approach is able to obtain better performance than two state of the art approaches: NSGAII and PMSMO.
In this paper, we propose a segmentation method based on region growing and evolutionary algorithms. Before segmentation, the number of classes is determined by the principle of maximum entropy. The proposed approach ...
详细信息
In this paper, we propose a segmentation method based on region growing and evolutionary algorithms. Before segmentation, the number of classes is determined by the principle of maximum entropy. The proposed approach is validated on some synthetic and real images and, it shows to be very interesting as decision support in quality control.
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