The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. The...
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The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.
Software product line (SPL) engineering methodology assist to create a range of software products within less time and cost but with high quality by the reuse of core software assets, which has been tested. Thus, test...
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Software product line (SPL) engineering methodology assist to create a range of software products within less time and cost but with high quality by the reuse of core software assets, which has been tested. Thus, testing is crucial for successfully deploying SPL. As the product features increases, testing process can be time-consuming. Testing in SPL is regarded as a combinatorial optimization problem. Evolutionary algorithms were reported to provide good results in such class of problems. This research provides a framework to compare different multi-objective Evolutionary algorithms performance regarding software product line context. We report on the problem encoding, variation operators and different types of algorithms: Indicator Based Evolutionary Algorithm (IBEA), Strength Pareto Evolutionary algorithm II (SPEA-II), multi-objective Evolutionary algorithms based on Decomposition (MOEA/D) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The framework will provide preliminary results on different Feature Models (FMs) to measure their feasibility to optimize SPL testing.
Organic electrochemical synthesis may be combined with continuous flow and automation technology. Here, the authors highlight the benefits of such chemical engineering approaches along with technology and software dir...
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