Titlebook: Soft Computing Models in Industrial and Environmental Applications; 7th International Co Václav Snášel,Ajith Abraham,Emilio S. Corchado Con

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Differential Evolution Classifier with Optimized Distance Measures for the Features in the Data Setase we combine the individually measured distances from each feature to form an overall distance measure between the class prototype vectors and sample. Each sample is then classified to the class assigned with the nearest prototype vector using that overall distance measure. The proposed approach i

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2194-5357 ce on Soft Computing Models in Industrial and Environmental .This volume of Advances in Intelligent and Soft Computing contains accepted papers presented at SOCO 2012, held in the beautiful and historic city of Ostrava (Czech Republic), in September 2012.. .Soft computing represents a collection or

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A Hybrid Discrete Differential Evolution Algorithm for Economic Lot Scheduling Problem with Time Vaied. In this problem, several products compete for the use of a single machine, which is very similar to the real-life industrial scenario, in particular in the field of remanufacturing. The experimental results indicate that the proposed algorithm outperforms several previously used heuristic algorithms under the time-varying lot sizing approach.

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An Ordinal Regression Approach for the Unequal Area Facility Layout Problem,xperts: {.. To do so, we will also make an approximation to some of the most successful ordinal classification methods in the machine learning literature. The best model obtained will be used in order to guide the searching of a genetic algorithm for generating new facility layouts.

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A Soft Computing Approach to Knowledge Flow Synthesis and Optimization,nary approach to automated KF synthesis and optimization. We demonstrate the evolutionary KF synthesis on the problem of classifier construction. Both preprocessing and machine learning actions are selected and configured by means of evolution to produce a model that fits very well for a given dataset.

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