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To find an optimized structure in a Bayesian network is a NP problem. How to get a network with a high score is an important question. In this paper, we discuss some theories about Bayesian network study and propose a hybrid genetic algorithm HGA-BN for Bayesian network optimization. The algorithm is based on genetic algorithm, uses simulated annealing technology to select its children, and uses self-adaptive...
This paper presents an evolution-based hierarchical feature fusion system that selects the dominant features among multiple feature vectors for ultrasonic liver tissue characterization. After extracting the spatial gray-level dependence matrices, multiresolution fractal feature vectors and multiresolution energy feature vectors, the system utilizes evolution-based algorithms to select features. In...
This work describes the feasibility of multiresolution feature selection and its application to classify ultrasonic liver images. The proposed approach uses genetic algorithm and defines a novel fitness function for medical applications since the diagnosis correctness is the most important consideration. Via the measurement of class seperability, we can uniquely select the feature vector. The effectiveness...
Automated negotiation is one of the key issues in e-commerce. For the purpose of better expressing Agent's requirement and preference, easier dealing with issues inter-dependence and trade-off, this paper analyze fuzzy constraints' significances to e-commerce negotiation detailedly, then proposed a general automated negotiation model based on prioritised fuzzy constraint satisfaction problem (PFCSP),...
Aiming at Automated Guided Vehicle (AGV) dynamic model characteristics, a Variable Structure Control based on genetic algorithm (GA) and least square-support vector machine (LS-SVM) was designed. Parameters, predetermined by conventional reaching law, were regulated by LS-SVM online. It was shown that system shattering is eliminated. Simulation results indicated that this method possesses the advantages...
Image segmentation is the key step for quantitative analysis of brain tissues (white matter, gray matter and cerebrospinal fluid). Based on genetic algorithm and fuzzy C-means (FCM) approach, a fast and fully automatic segmentation method of brain tissues named genetic fuzzy clustering algorithm is introduced in this paper. The method operates slice by slice based on three main steps: The non-brain...
In the quantitative analysis of brain tissues (white matter, gray matter and cerebrospinal fluid) in magnetic resonance (MR) brain images, segmentation is the preliminary step. This paper proposes a novel segmentation method based on k-means objective function combined genetic algorithm, which is known for its global optimum searching ability. The method operates slice by slice via three main steps:...
Identification of transcription factor binding sites from the upstream regions of genes is a highly important and unsolved problem. In this paper, we propose a novel framework for using evolutionary algorithm to solve this challenging issue. Under this framework, we use two prevalent evolutionary algorithms: genetic algorithm (GA) and particle swarm optimization (PSO) to find unknown sites in a collection...
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