A Framework for Genetic Algorithms Based on Hadoop

Genetic Algorithms (GAs) are powerful metaheuristic techniques mostly used in many real-world applications. The sequential execution of GAs requires considerable computational power both in time and resources. Nevertheless, GAs are naturally parallel and accessing a parallel platform such as Cloud is easy and cheap. Apache Hadoop is one of the common services that can be used for parallel applications. However, using Hadoop to develop a parallel version of GAs is not simple without facing its inner workings. Even though some sequential frameworks for GAs already exist, there is no framework supporting the development of GA applications that can be executed in parallel. In this paper is described a framework for parallel GAs on the Hadoop platform, following the paradigm of MapReduce. The main purpose of this framework is to allow the user to focus on the aspects of GA that are specific to the problem to be addressed, being sure that this task is going to be correctly executed on the Cloud with a good performance. The framework has been also exploited to develop an application for Feature Subset Selection problem. A preliminary analysis of the performance of the developed GA application has been performed using three datasets and shown very promising performance

System optimization by multiobjective genetic algorithms and analysis of the coupling between variables, constraints and objectives

This paper presents a methodology based on Multiobjective Genetic Algorithms (MOGA’s) for the design of electrical engineering systems. MOGA’s allow to optimize multiple heterogeneous criteria in complex systems, but also simplify couplings and sensitivity analysis by determining the evolution of design variables along the Pareto-optimal front. A rather simplified case study dealing with the optimal dimensioning of an inverter – permanent magnet motor – reducer – load association is carried out to demonstrate the interest of the approach

Energy efficiency measurement procedure for gearboxes in their entire operating range

Over the last decade, forced regulations and a growing social awareness with respect to energy efficiency have resulted in a renewed interest in the research for high efficient electrical machines. When an electrical motor is coupled to a machine, in many cases a gearbox or belt transmission is used. Research shows a lack of information on energy efficiency of these components. In comparison to electrical motors and drives, there is very few regulation and if efficiency values can be found in catalogues, there is no regulated test procedure available to validate the data. As a result, the reliability of these efficiency values is unknown and comparison between manufacturers and technologies is impossible. In this paper a test bench is proposed to measure the energy efficiency of a gearbox with an accuracy up to 0.4%. The test bench is used to measure the efficiency of gearboxes in their entire speed and torque range. Contour maps are used to visualize these measurement results. Moreover, a measurement campaign using different gearboxes is carried out to compare the energy efficiency in the manufacturers catalogue and the measured efficiency

Two-Stage Eagle Strategy with Differential Evolution

Efficiency of an optimization process is largely determined by the search algorithm and its fundamental characteristics. In a given optimization, a single type of algorithm is used in most applications. In this paper, we will investigate the Eagle Strategy recently developed for global optimization, which uses a two-stage strategy by combing two different algorithms to improve the overall search efficiency. We will discuss this strategy with differential evolution and then evaluate their performance by solving real-world optimization problems such as pressure vessel and speed reducer design. Results suggest that we can reduce the computing effort by a factor of up to 10 in many applications

A Design Method to Exploit Synergies Between Fiber-Reinforce Composites and Additive Manufactured Processes

This paper proposes a design method for devices composed of long fiber-reinforced composites (FRC) and additive manufactured (AM) parts. Both FRC and AM processes have similar application characteristics: suitable for small production volumes, additive in nature, and capable of being highly automated. On the other hand, the classes have distinct characteristics. FRCcomponents tend to be large and of simple shapes, while AM components tend to be small with highly complex geometry. Their combination has the potential for significant synergies, while mitigating their individual limitations. A decision guide is proposed, in the form of a series of questions, to guide the designer to determine if their application is a good candidate for FRC+AM. The decision guide is reformulated into a proposed design process that guides the designer to advantageously benefit from AM and FRC characteristics. The tools are illustrated with an example of a composite pressure vessel with integrated pressure reducer

Development of SED Camera for Quasars in Early Universe (SQUEAN)

We describe the characteristics and performance of a camera system, Spectral energy distribution Camera for Quasars in Early Universe (SQUEAN). It was developed to measure SEDs of high redshift quasar candidates (z $\gtrsim$ 5) and other targets, e.g., young stellar objects, supernovae, and gamma-ray bursts, and to trace the time variability of SEDs of objects such as active galactic nuclei (AGNs). SQUEAN consists of an on-axis focal plane camera module, an auto-guiding system, and mechanical supporting structures. The science camera module is composed of a focal reducer, a customizable filter wheel, and a CCD camera on the focal plane. The filter wheel uses filter cartridges that can house filters with different shapes and sizes, enabling the filter wheel to hold twenty filters of 50 mm $\times$ 50 mm size, ten filters of 86 mm $\times$ 86 mm size, or many other combinations. The initial filter mask was applied to calibrate the filter wheel with high accuracy and we verified that the filter position is repeatable at much less than one pixel accuracy. We installed and tested 50 nm medium bandwidth filters of 600 -- 1,050 nm and other filters at the commissioning observation in 2015 February. We found that SQUEAN can reach limiting magnitudes of 23.3 - 25.3 AB mag at 5-$\sigma$ in a 1-hour total integration time. - 25.3 AB mag at 5-$\sigma$ in a 1-hour total integration time.Comment: 31 pages, 14 figure

BigFCM: Fast, Precise and Scalable FCM on Hadoop

Clustering plays an important role in mining big data both as a modeling technique and a preprocessing step in many data mining process implementations. Fuzzy clustering provides more flexibility than non-fuzzy methods by allowing each data record to belong to more than one cluster to some degree. However, a serious challenge in fuzzy clustering is the lack of scalability. Massive datasets in emerging fields such as geosciences, biology and networking do require parallel and distributed computations with high performance to solve real-world problems. Although some clustering methods are already improved to execute on big data platforms, but their execution time is highly increased for large datasets. In this paper, a scalable Fuzzy C-Means (FCM) clustering named BigFCM is proposed and designed for the Hadoop distributed data platform. Based on the map-reduce programming model, it exploits several mechanisms including an efficient caching design to achieve several orders of magnitude reduction in execution time. Extensive evaluation over multi-gigabyte datasets shows that BigFCM is scalable while it preserves the quality of clustering

Distributed multinomial regression

This article introduces a model-based approach to distributed computing for multinomial logistic (softmax) regression. We treat counts for each response category as independent Poisson regressions via plug-in estimates for fixed effects shared across categories. The work is driven by the high-dimensional-response multinomial models that are used in analysis of a large number of random counts. Our motivating applications are in text analysis, where documents are tokenized and the token counts are modeled as arising from a multinomial dependent upon document attributes. We estimate such models for a publicly available data set of reviews from Yelp, with text regressed onto a large set of explanatory variables (user, business, and rating information). The fitted models serve as a basis for exploring the connection between words and variables of interest, for reducing dimension into supervised factor scores, and for prediction. We argue that the approach herein provides an attractive option for social scientists and other text analysts who wish to bring familiar regression tools to bear on text data.Comment: Published at http://dx.doi.org/10.1214/15-AOAS831 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Products as arrays of cues : how do consumers evaluate competing brands?

School of Managemen