Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems

Recently, researches have shown that the performance of metaheuristics can be affected by population initialization. Opposition-based Differential Evolution (ODE), Quasi-Oppositional Differential Evolution (QODE), and Uniform-Quasi-Opposition Differential Evolution (UQODE) are three state-of-the-art methods that improve the performance of the Differential Evolution algorithm based on population initialization and different search strategies. In a different approach to achieve similar results, this paper presents a technique to discover promising regions in a continuous search-space of an optimization problem. Using machine-learning techniques, the algorithm named Smart Sampling (SS) finds regions with high possibility of containing a global optimum. Next, a metaheuristic can be initialized inside each region to find that optimum. SS and DE were combined (originating the SSDE algorithm) to evaluate our approach, and experiments were conducted in the same set of benchmark functions used by ODE, QODE and UQODE authors. Results have shown that the total number of function evaluations required by DE to reach the global optimum can be significantly reduced and that the success rate improves if SS is employed first. Such results are also in consonance with results from the literature, stating the importance of an adequate starting population. Moreover, SS presents better efficacy to find initial populations of superior quality when compared to the other three algorithms that employ oppositional learning. Finally and most important, the SS performance in finding promising regions is independent of the employed metaheuristic with which SS is combined, making SS suitable to improve the performance of a large variety of optimization techniques. (C) 2012 Elsevier Inc. All rights reserved.

The process of constructing new competencies in the family agricultural production unit

This article discusses the difficulties dairy farmers face when they decide to install a new type of production on their units. We intend to discuss the nature of the new competencies the farmers will construct in order to install new production ateliers, and to show the complexity of the means they used, the difficulties they face in this process, and the strategies farmers develop in consonance with the practical knowledge of their profession. The method used was Ergonomic Work Analysis, together with semi-structured interviews, done after sessions of observation and work analysis. The results show that it is possible to apprehend a part of the complexity of the process of constructing competencies among dairy farmers, the diversity of kinds of resources they mobilize, integrate and transfer in this construction process that materializes through their activities in the work context.