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

The BMOM is particularly useful for obtaining post-data moments and densities for parameters and future observations when the form of the likelihood function is unknown and thus a traditional Bayesian approach cannot be used. Also, even when the form of the likelihood is assumed known, in time series problems it is sometimes difficult to formulate an appropriate prior density. Here, we show how the BMOM approach can be used in two, nontraditional problems. The first one is conditional forecasting in regression and time series autoregressive models. Specifically, it is shown that when forecasting disaggregated data (say quarterly data) and given aggregate constraints (say in terms of annual data) it is possible to apply a Bayesian approach to derive conditional forecasts in the multiple regression model. The types of constraints (conditioning) usually considered are that the sum, or the average, of the forecasts equals a given value. This kind of condition can be applied to forecasting quarterly values whose sum must be equal to a given annual value. Analogous results are obtained for AR(p) models. The second problem we analyse is the issue of aggregation and disaggregation of data in relation to predictive precision and modelling. Predictive densities are derived for future aggregate values by means of the BMOM based on a model for disaggregated data. They are then compared with those derived based on aggregated data. Copyright © 2006 John Wiley & Sons, Ltd.

The Bayesian method of moments (BMOM) in some aggregation problems in econometrics

Given a group of people traveling from the same origini¾?to multiple destinations, the Taxi Sharing Problem consists in assigning taxis to each person such that the total cost spent by the group of people is minimized. This problem arises in the context of Smart Mobility, where the resources of a city must be optimized to save costs and pollution while the mobility services are improved for the citizens. We propose a mixed integer linear programming formulation as an accurate way to solve the problem of taxi sharing. We empirically analyze our formulation solving different real-like instances of the problem with 9 to 69 people.

Mixed Integer Linear Programming Formulation for the Taxi Sharing Problem

This article presents the problem of locating electric vehicle (EV) charging stations in a city by defining the Electric Vehicle Charging Stations Locations (EV-CSL) problem. The idea is to minimize the distance the citizens have to travel to charge their vehicles. EV-CSL takes into account the maximum number of charging stations to install and the electric power requirements. Two metaheuristics are applied to address the relying optimization problem: a genetic algorithm (GA) and a variable neighborhood search (VNS). The experimental analysis over a realistic scenario of Malaga city, Spain, shows that the metaheuristics are able to find competitive solutions which dramatically improve the actual installation of the stations in Malaga. GA provided statistically the best results.

Citizen Centric Optimal Electric Vehicle Charging Stations Locations in a Full City: Case of Malaga

This paper presents a new mathematical approach to study the behaviour of population-based methods. The calculation of the takeover time and the dynamical growth curves is a common analytical approach to measure the selection pressure of an EA and any algorithm which manipulates a set of solutions. In this work, we propose a new and more accurate model to calculate these values. This new model also includes other very interesting features, such as the characterization of the complete behaviour of the methods using a single value, the Rayleigh distribution parameter. We also extend the study to consider the effect of the mutation (or in general, any neighborhood exploration operator) and we show several advanced uses of this models such as building self-adaptive techniques or comparing algorithms.

Analyzing the behaviour of population-based algorithms using rayleigh distribution

The sequencing process of a DNA chain for reading its components supposes a complex process, since only small DNA fragments can be read nowadays. Therefore, the use of optimization algorithms is required to rebuild a single chain from all the small pieces. We address here a simplified version of the problem, in which no errors in the sequencing process are allowed. The methods typically used in the literature for this problem are not satisfactory when solving realistic size instances, so there is a need for new more efficient and accurate methods. We propose a new iterated local search algorithm, highly competitive with the best algorithms in the literature, and considerably faster.

Enrique Alba

Papers

The Bayesian method of moments (BMOM) in some aggregation problems in econometrics

Mixed Integer Linear Programming Formulation for the Taxi Sharing Problem

Citizen Centric Optimal Electric Vehicle Charging Stations Locations in a Full City: Case of Malaga

Analyzing the behaviour of population-based algorithms using rayleigh distribution

Iterated local search for de novo genomic sequencing