Titlebook: Research and Development in Intelligent Systems XXIII; Proceedings of AI-20 Max Bramer,Frans Coenen,Andrew Tuson Conference proceedings 200

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Initialization Method for Grammar-Guided Genetic Programmingd method have a higher convergence speed. Two different problems have been chosen to carry out the experiments: a laboratory test involving searching for arithmetical equalities and the real-world task of breast cancer prognosis. In both problems, comparisons have been made to another five important initialization methods.

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Argument Based Contract Enforcementts an agent utility, and our framework operates by using a reasoning mechanism which is based on the agent comparing the utility it would gain for proving a set of literals with the costs incurred during this process.

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Avoiding Long and Fruitless Dialogues in Critiquingsm based on implicit relaxation of constraints ensures that progress can again be made if the user is willing to compromise. Our empirical results show that progressive critiquing is most effective when users give priority to critiques on attributes whose values they are least inclined to accept.

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Initialization Method for Grammar-Guided Genetic Programming trees to be generated. An important feature of this algorithm is that the initial populations generated are adequately distributed in terms of tree size and distribution within the search space. Consequently, genetic programming systems starting from the initial populations generated by the propose

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Improving the Gradient Based Search Direction to Enhance Training Efficiency of Back Propagation Basradient based search direction. A novel approach is presented in this paper for improving the training efficiency of back propagation neural network algorithms by adaptively modifying this gradient based search direction. The proposed algorithm uses the value of gain parameter in the activation func

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A Decision Tree-Based Attribute Weighting Filter for Naive Bayesnhancements to the basic algorithm have been proposed to help mitigate its primary weakness—the assumption that attributes are independent given the class. All of them improve the performance of naive Bayes at the expense (to a greater or lesser degree) of execution time and/or simplicity of the fin

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