Training neural networks is a complex task of great importance in the supervised learning field of research. We intend to show the superiority (time performance and quality of solution) of the new metaheuristic bat algorithm (BA) over other more-standard‖ algorithms in neural network training. In this work we tackle this problem with five algorithms, and try to over a set of results that could hopefully foster future comparisons by using a standard dataset (Proben1: selected benchmark composed of problems arising in the field of Medicine) and presentation of the results. We have selected two gradient descent algorithms: Back propagation and Levenberg-Marquardt, and three population based heuristic: Bat Algorithm, Genetic Algorithm, and Particle Swarm Optimization. Our conclusions clearly establish the advantages of the new metaheuristic bat algorithm over the other algorithms in the context of eLearning.

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Abstract.A method for screening of company workplaces with high ergonomic risk is developed. For clustering of company workplaces a fuzzy modification of bat algorithm is proposed. Using data gathered by a checklist from workplaces, information for ergonomic related health risks is extracted. Three clusters of workplaces with low, moderate and high ergonomic risk are determined. Using these clusters, workplaces with moderate and high ergonomic risk levels are screened and relevant solutions are proposed. By a case study this method is illustrated and validated. Important advantages of the method are reduction of computational effort and fast screening of workplaces with major ergonomic problems within a company.

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The use of ratio and product methods of estimation using auxiliary information for estimating the mean of a finite population is well known. Srivastava [1967] and Reddy [19731 proposed ratio-cum-product type estimators. This paper proposes a transformed estimator which is even more efficient than these estimators for a wide range of the value of the correlation coefficient between the main and auxiliary variables.

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