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.

/pdf/differential-evolution-a-survey-of-the-state-of-the-art-3lz840pz2t.pdf

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

This paper presents a theoretical study of the selection pressure in asynchronous cellular evolutionary algorithms (cEAs). This work is motivated by the search for a general model for asynchronous update of the individuals in a cellular EA, and by the necessity of better accuracy beyond what existing models of selection intensity can provide. Therefore, we investigate the differences between the expected and actual values of the selection pressure induced by several asynchronous update policies, and formally characterize the update dynamics of each variant of the algorithm. New models for these two issues are proposed, and are shown to be more accurate (lower fit error) than previous ones.

/pdf/selection-intensity-in-asynchronous-cellular-evolutionary-1sc99293mw.pdf

Selection intensity in asynchronous cellular evolutionary algorithms

The Software Project Scheduling (SPS) problem relates to the decision of who does what during a software project lifetime. This problem has a capital importance for software companies. In the SPS problem, the total budget and human resources involved in software development must be optimally managed in order to end up with a successful project. Companies are mainly concerned with reducing both the duration and the cost of the projects, and these two goals are in conflict with each other. A multi-objective approach is therefore the natural way of facing the SPS problem. In this paper, a number of multi-objective metaheuristics have been used to address this problem. They have been thoroughly compared over a set of 36 publicly available instances that cover a wide range of different scenarios. The resulting project schedulings of the algorithms have been analyzed in order to show their relevant features. The algorithms used in this paper and the analysis performed may assist project managers in the difficult task of deciding who does what in a software project.

Using multi-objective metaheuristics to solve the software project scheduling problem

In this article, we encompass an analysis of the recent advances in parallel genetic algorithms (PGAs). We have selected these algorithms because of the deep interest in many research fields for techniques that can face complex applications where running times and other computational resources are greedily consumed by present solvers, and PGAs act then as efficient procedures that fully use modern computational platforms at the same time that allow the resolution of cutting-edge open problems. We have faced this survey on PGAs with the aim of helping newcomers or busy researchers who want to have a wide vision on the field. Then, we discuss the most well-known models and their implementations from a recent (last six years) and useful point of view: We discuss on highly cited articles, keywords, the venues where they can be found, a very comprehensive (and new) taxonomy covering different research domains involved in PGAs, and a set of recent applications. We also introduce a new vision on open challenges and try to give hints that guide practitioners and specialized researchers. Our conclusion is that there are many advantages to using these techniques and lots of potential interactions to other evolutionary algorithms; as well, we contribute to creating a body of knowledge in PGAs by summarizing them in a structured way, so the reader can find this article useful for practical research, graduate teaching, and as a pedagogical guide to this exciting domain.

Parallel Genetic Algorithms: A Useful Survey

Introduction to Cellular Genetic Algorithms

In this paper we address the physical parallelization of a very efficient genetic algorithm (GA) known as gradual distributed real-coded GA (GD-RCGA). This search model naturally provides a set of eight sub-populations residing in a cube topology having two faces for promoting exploration and exploitation. The resulting technique has been shown to yield very accurate results on continuous optimization by using crossover operators tuned to exploit and explore the space inside each sub-population. Here, we encompass the first actual parallelization of the technique, and get deeper into the importance of running a synchronous versus an asynchronous version of the basic GD-RCGA model. Our results indicate that this model maintains a very high level of accuracy for continuous optimization when run in parallel, as well as we show the similarities between the sync and async versions. Finally, we show that async parallelization is really more scalable than the sync one, suggesting future research lines for WAN execution and new models of search based in the two-faced cube of the original model.

/pdf/parallel-heterogeneous-genetic-algorithms-for-continuous-2920q1039b.pdf

Enrique Alba

Papers

Selection intensity in asynchronous cellular evolutionary algorithms

Using multi-objective metaheuristics to solve the software project scheduling problem

Parallel Genetic Algorithms: A Useful Survey

Introduction to Cellular Genetic Algorithms

Parallel heterogeneous genetic algorithms for continuous optimization