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Abstract: This paper presents a method to solve disassembly sequence planning problem. Firstly, disassembly feasibility information graph (DFIG) is presented to describe the product disassembly sequence and operation information. Then, disassembly sequence planning problem is mapped onto the DFIG as an optimal path-searching problem. Based on this idea, a genetic algorithm is provided to find out feasible and optimal disassembly solutions efficiently. Finally, a case study, which is used to illustrate the performance of proposed method, is presented. [Copyright &y& Elsevier]

Abstract: Gene prediction is still an important step to annotate genomes. In this paper, we proposed a novel method for recognizing gene in genomes. The method combines three famous gene-finding programs. After calculating the accuracy parameters, the equitable weight for each parameter is calculated using genetic algorithm. Then the integrative evaluation is performed. The integrative evaluation is employed to instruct the training of an artificial neural network. The simulation results show that the proposed method integrates advantages of three programs and the accuracy has an obvious improvement, which indicate that the proposed method has a powerful capability for gene prediction. [Copyright &y& Elsevier]

Abstract: The classification ability of a single-layer perceptron could be improved by considering some enhanced features. In particular, this form of neural networks is called a functional-link net. In the output neuron''s activation function, such as the sigmoid function, an inner product of a connection weight vector with an input vector is computed. However, since the input features are not independent of each other for the enhanced pattern, an assumption of the additivity is not reasonable. This paper employs a non-additive technique, namely the fuzzy integral, to aggregate performance values for an input pattern by interpreting each of the connection weights as a fuzzy measure of the corresponding feature. A learning algorithm with the genetic algorithm is then designed to automatically find connection weights. The sample data for bankruptcy analysis obtained from Moody''s Industrial Manuals is considered to examine the classification ability of the proposed method. The results demonstrate that the proposed method performs well in comparison with traditional functional-link net and multivariate techniques. [Copyright &y& Elsevier]