THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Grey Wolf Optimizer

Data Mining Practical Machine Learning Tools and Techniques

The Whale Optimization Algorithm

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

Remote Optimization Service

This paper introduces a new cellular genetic algorithm for solving multiobjective continuous optimization problems. Our approach is characterized by using an external archive to store nondominated solutions and a feedback mechanism in which solutions from this archive randomly replace existing individuals in the population after each iteration. The result is a simple and elitist algorithm called MOCell. Our proposal has been evaluated with both constrained and unconstrained problems and compared against NSGA-II and SPEA2, two state-of-the-art evolutionary multiobjective optimizers. For the studied benchmark, our experiments indicate that MOCell obtains competitive results in terms of convergence and hypervolume, and it clearly outperforms the other two compared algorithms concerning the diversity of the solutions along the Pareto front. C � 2009 Wiley Periodicals, Inc.

Author Proof MOCell: A Cellular Genetic Algorithm

The smart city concept refers principally to employing technology to deal with different problems surrounding the city and the citizens. Urban mobility is one of the most challenging aspects considering the logistical complexity as well as the ecological relapses. More specifically, parking is a daily tedious task that citizens confront especially considering the large number of vehicles compared to the limited parking, the rush hours peaks, etc. Forecasting parking occupancy might allow citizens to plan their parking better and therefore enhance their mobility. Time-series forecasting methods have proved their efficiency for such tasks, and this work goes in the same line by exploring how to provide more accurate parking occupancy forecasting. Concretely, its contributions stand in a complete pipeline, including (I) the automatic extra-transform-load data module and (II) the time-series forecasting methods themselves, where four have been studied: one additive regression model (Prophet), the Seasonal Auto-Regressive Integrated Moving Average (SARIMAX), and two deep learning models, the Long Short Term memory neural networks (LSTM), and Neural Prophet. Experiments have been performed on data of 3 and 28 parking from the city of Malaga (Spain) and Birmingham (England) using data recorded through 6 months (June-November 2022) and two and a half months (October-December, 2016), for Malaga and Birmingham, respectively. The results showed that Prophet provided very competitive results compared to the literature. 

Time Series Forecasting for Parking Occupancy: Case Study of Malaga and Birmingham Cities


 Solving problems of high dimensionality (and complexity) usually needs the intense use of technologies, like parallelism, advanced computers and new types of algorithms. MapReduce (MR) is a computing paradigm long time existing in computer science that has been proposed in the last years for dealing with big data applications, though it could also be used for many other tasks. In this article we address big optimization: the solution to large instances of combinatorial optimization problems by using MR as the paradigm to design solvers that allow transparent runs on a varied number of computers that collaborate to find the problem solution. We first investigate the influence of the used MR technology, then including Hadoop, Spark and MPI as the middleware plataforms to express genetic algorithms (GAs), arising the MRGA solvers, in a style different from the usual imperative transformational programming. Our objective is to confirm the expected benefits of these systems, namely file, memory and communication management, over the resulting algorithms. We analyze our MRGA solvers from relevant points of view like scalability, speedup, and communication vs. computation time in big optimization. The results for high dimensional datasets show that the MRGA over Hadoop outperforms the implementations in Spark and MPI frameworks. For the smallest datasets, the execution of MRGA on MPI is always faster than the executions of the remaining MRGAs. Finally, the MRGA over Spark presents the lowest communication times. Numerical and time insights are given in our work, so as to ease future comparisons of new algorithms over these three popular technologies.

/pdf/big-optimization-with-genetic-algorithms-hadoop-spark-and-122ey38c1c.pdf

Big Optimization with Genetic Algorithms: Hadoop, Spark and MPI

/pdf/metaheuristicas-aplicadas-a-la-resolucion-del-problema-de-414rq7zwg3.pdf

Enrique Alba

Papers

Remote Optimization Service

Author Proof MOCell: A Cellular Genetic Algorithm

Time Series Forecasting for Parking Occupancy: Case Study of Malaga and Birmingham Cities

Big Optimization with Genetic Algorithms: Hadoop, Spark and MPI

Metaheurísticas aplicadas a la resolución del problema de ensamblado de fragmentos de ADN