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

DNA Fragment Assembly Using Grid Systems

Un estudio de la potencia computacional y robustez de los algoritmos evolutivos paralelos

This article by Enrique de Alba and Manuel Mendoza extends Foresights previous coverage of methods for forecasting seasonal data when the historical series is short (less than 2-3 years of data). The authors describe and illustrate a Bayesian method for seasonal data and show that it can outperform traditional time series methods for short time series. Copyright International Institute of Forecasters, 2007

Bayesian Forecasting Methods for Short Time Series

This tutorial is intended to provide a comprehensive and broad overview of the main techniques, algorithms, application issues and software in relation with the growing area of evolutionary computation (EC). It includes in an on-line fashion a flexible access to different chapters containing explanations, examples and demos on the different algorithm families embedded in the EC-like search mechanisms. The tutorial is highly structured in chapters and sections, and also it offers a bibliography on the matter, as well as descriptions of concrete applications and theoretical details on evolutionary algorithms. The dynamic nature of such an info suggests the advantages of an on-line format. This allows a continuous improvement by the addition of results, graphs and new links to related material.

On Line Tutorial on Evolutionary Computation

Metaheuristics for solving multiobjective problems can provide an approximation of the Pareto front in a short time, but can also have difficulties finding feasible solutions in constrained problems. Integer linear programming solvers, on the other hand, are good at finding feasible solutions, but they can require some time to find and guarantee the efficient solutions of the problem. In this work we combine these two ideas to propose a hybrid algorithm mixing an exploration heuristic for multiobjective optimization with integer linear programming to solve multiobjective problems with binary variables and linear constraints. The algorithm has been designed to provide an approximation of the Pareto front that is well-spread throughout the objective space. In order to check the performance, we compare it with three popular metaheuristics using two benchmarks of multiobjective binary constrained problems. The results show that the proposed approach provides better performance than the baseline algorithms in terms of number of the solutions, hypervolume, generational distance, inverted generational distance, and the additive epsilon indicator.

Enrique Alba

Papers

DNA Fragment Assembly Using Grid Systems

Un estudio de la potencia computacional y robustez de los algoritmos evolutivos paralelos

Bayesian Forecasting Methods for Short Time Series

On Line Tutorial on Evolutionary Computation

Improving Search Efficiency and Diversity of Solutions in Multiobjective Binary Optimization by Using Metaheuristics Plus Integer Linear Programming