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

From the Publisher:
This book explores the meta-heuristics approach called tabu search, which is dramatically changing our ability to solve a hostof problems that stretch over the realms of resource planning,telecommunications, VLSI design, financial analysis, scheduling, spaceplanning, energy distribution, molecular engineering, logistics,pattern classification, flexible manufacturing, waste management,mineral exploration, biomedical analysis, environmental conservationand scores of other problems. The major ideas of tabu search arepresented with examples that show their relevance to multipleapplications. Numerous illustrations and diagrams are used to clarifyprinciples that deserve emphasis, and that have not always been wellunderstood or applied. The book's goal is to provide ''hands-on' knowledge and insight alike, rather than to focus exclusively eitheron computational recipes or on abstract themes. This book is designedto be useful and accessible to researchers and practitioners inmanagement science, industrial engineering, economics, and computerscience. It can appropriately be used as a textbook in a masterscourse or in a doctoral seminar. Because of its emphasis on presentingideas through illustrations and diagrams, and on identifyingassociated practical applications, it can also be used as asupplementary text in upper division undergraduate courses. 

Finally, there are many more applications of tabu search than canpossibly be covered in a single book, and new ones are emerging everyday. The book's goal is to provide a grounding in the essential ideasof tabu search that will allow readers to create successfulapplications of their own. Along with the essentialideas,understanding of advanced issues is provided, enabling researchers togo beyond today's developments and create the methods of tomorrow.

Tabu Search

https://sci2s.ugr.es/sites/default/files/files/Teaching/GraduatesCourses/Metaheuristicas/Bibliography/VNS.pdf

Variable neighborhood search

The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.

/pdf/metaheuristics-in-combinatorial-optimization-overview-and-menr0q3bxw.pdf

Metaheuristics in combinatorial optimization: Overview and conceptual comparison

Variable Neighborhood Search.

Part 1 Introduction: combinatorial problems local and global optima heuristics. Part 2 Simulated annealing: the basic method enhancements and modifications applications conclusions. Part 3 Tabu search: the tabu framework broader aspects of intensification and diversification tabu search applications connections and conclusions. Part 4 Genetic algorithms: basic concepts a simple example extensions and modifications applications conclusions. Part 5 Artificial neural networks: neural networks combinatorial optimization problems the graph bisection problem the graph partition problem the travelling salesman problem scheduling problems deformable templates inequality constraints, the Knapsack problem summary. Part 6 Lagrangian relaxation: overview basic methodology Lagrangian heuristics and problem reduction determination of Lagrange multipliers dual ascent tree search applications conclusions. Part 7 Evaluation of heuristic performance: analytical methods empirical testing statistical inference conclusions.

Modern heuristic techniques for combinatorial problems

While Lawrence Fogel, John Holland, Ingo Rechenberg and others were the undoubted pioneers of the field we now know as evolutionary algorithms (EA), or evolutionary computation (EC), Ken De Jong’s doctoral thesis of 1975 deserves much of the credit for firing the enthusiasm of several research communities in the practical exploration of these methods. Moreover, as he has taken a very active part in the development of the field through the last 30 years, there could scarcely be anyone better placed to write a book on evolutionary computation. As the subtitle of his book promises, De Jong takes a unified approach. His first 4 chapters carefully explain and differentiate, whilst putting in their historical context, the common aspects of different EC paradigms (evolutionary programming—EP, evolution strategies—ES and genetic algorithms—GA). Chapters 1–4 use clear examples, rather than too many mathematical symbols. They form a truly superb introduction. Any novice coming to EC should come away with an excellent grasp of the basics. In chapter 5 he discusses the different uses to which EAs have been put as problem-solvers. The greater part is devoted to optimization (OPT-EA), with shorter sections on search, machine learning, and automated programming. There is a final, very brief, section on adaptive EAs. In the optimization part, considerable care is taken in the organisation of his material—again, presumably, with the novice in mind. Chapter 6 is the longest, and focuses on EC theory. De Jong carefully builds up a picture of the influences of selection, mutation and recombination on the behaviour of EAs. If you are expecting theory in the sense of a comprehensive, general model with well-understood effects, you will be disappointed. There are equations, but the argument is in fact founded on a series of experiments, whose results are displayed in a series of graphs. That is not to say that the insights gained are incorrect, or

Evolutionary computation: a unified approach

In a previous paper, a simple genetic algorithm (GA) was developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem (PFSP). The performance of the algorithm was comparable to that of a naive neighborhood search technique and a proven simulated annealing algorithm. However, recent results have demonstrated the superiority of a tabu search method in solving the PFSP. In this paper, we reconsider the implementation of a GA for this problem and show that by taking into account the features of the landscape generated by the operators used, we are able to improve its performance significantly.

/pdf/genetic-algorithms-path-relinking-and-the-flowshop-rkcl6ipzty.pdf

Genetic algorithms, path relinking, and the flowshop sequencing problem

Heuristic search methods have been increasingly applied to combinatorial optimizationproblems. While a specific problem defines a unique search space, different “landscapes”are created by the different heuristic search operators used to search it. In this paper, asimple example will be used to illustrate the fact that the landscape structure changes withthe operator; indeed, it often depends even on the way the operators are applied. Recentattention has focused on trying to better understand the nature of these “landscapes”. Recentwork by Boese et al. [2] has shown that instances of the TSP are often characterised by a“big valley” structure in the case of a 2‐opt exchange operator, and a particular distancemetric. In this paper, their work is developed by investigating the question of how landscapeschange under different search operators in the case of the n/m/P/Cmax flowshop problem.Six operators and four distance metrics are defined, and the resulting landscapes examined.The work is further extended by proposing a statistical randomisation test to provide anumerical assessment of the landscape. Other conclusions relate to the existence of ultra‐metricity,and to the usefulness or otherwise of hybrid neighbourhood operators.

Colin R. Reeves

Papers

Modern heuristic techniques for combinatorial problems

Evolutionary computation: a unified approach

Genetic algorithms, path relinking, and the flowshop sequencing problem

Landscapes, operators and heuristic search

Constructive and composite heuristic solutions to the P//∑Ci scheduling problem