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

In many engineering optimization problems, the number of function evaluations is severely limited by time or cost. These problems pose a special challenge to the field of global optimization, since existing methods often require more function evaluations than can be comfortably afforded. One way to address this challenge is to fit response surfaces to data collected by evaluating the objective and constraint functions at a few points. These surfaces can then be used for visualization, tradeoff analysis, and optimization. In this paper, we introduce the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering. We then show how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule. The key to using response surfaces for global optimization lies in balancing the need to exploit the approximating surface (by sampling where it is minimized) with the need to improve the approximation (by sampling where prediction error may be high). Striking this balance requires solving certain auxiliary problems which have previously been considered intractable, but we show how these computational obstacles can be overcome.

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Efficient Global Optimization of Expensive Black-Box Functions

Introduction to Optimization The Binary Genetic Algorithm The Continuous Parameter Genetic Algorithm Applications An Added Level of Sophistication Advanced Applications Evolutionary Trends Appendix Glossary Index.

https://cdn.preterhuman.net/texts/science_and_technology/nature_and_biology/Bioinformatics/Practical%20Genetic%20Algorithms%20-%20Randy%20L.%20Haupt,%20Sue%20Ellen%20Haupt.pdf

Practical Genetic Algorithms

Evolutionary algorithms often have to solve optimization problems in the presence of a wide range of uncertainties. Generally, uncertainties in evolutionary computation can be divided into the following four categories. First, the fitness function is noisy. Second, the design variables and/or the environmental parameters may change after optimization, and the quality of the obtained optimal solution should be robust against environmental changes or deviations from the optimal point. Third, the fitness function is approximated, which means that the fitness function suffers from approximation errors. Fourth, the optimum of the problem to be solved changes over time and, thus, the optimizer should be able to track the optimum continuously. In all these cases, additional measures must be taken so that evolutionary algorithms are still able to work satisfactorily. This paper attempts to provide a comprehensive overview of the related work within a unified framework, which has been scattered in a variety of research areas. Existing approaches to addressing different uncertainties are presented and discussed, and the relationship between the different categories of uncertainties are investigated. Finally, topics for future research are suggested.

/pdf/evolutionary-optimization-in-uncertain-environments-a-survey-1cgcnwsuec.pdf

Evolutionary optimization in uncertain environments-a survey

Evolutionary algorithms (EAs) have received increasing interests both in the academy and industry. One main difficulty in applying EAs to real-world applications is that EAs usually need a large number of fitness evaluations before a satisfying result can be obtained. However, fitness evaluations are not always straightforward in many real-world applications. Either an explicit fitness function does not exist, or the evaluation of the fitness is computationally very expensive. In both cases, it is necessary to estimate the fitness function by constructing an approximate model. In this paper, a comprehensive survey of the research on fitness approximation in evolutionary computation is presented. Main issues like approximation levels, approximate model management schemes, model construction techniques are reviewed. To conclude, open questions and interesting issues in the field are discussed.

https://epubs.surrey.ac.uk/7610/2/SC2005.pdf

A comprehensive survey of fitness approximation in evolutionary computation

Static and dynamic response of moderately thick laminated beams with damage

A new technical theory and associated finite element model is introduced for thick laminated and sandwich beams. The theory can be cast as a layerwise theory with high-order zig-zag sublaminate approximations, thus greatly reducing the number of degrees of freedom required to accurately describe the bending and transverse shear kinematics in thick laminates. Furthermore, the theory is adaptable, allowing the user to choose the number of sublaminate approximations to achieve the desired accuracy. Based on this theory, a simple, efficient, and robust finite element model is developed that has the nodal topology of a four-noded planar element yet has the advantages of beam-type kinematics and a special interpolation scheme that obviates locking. The element contains a single zig-zag sublaminate approximation. If desired, multiple elements can be used through the thickness of a laminate to increase accuracy.

Ronald C. Averill

Papers

Static and dynamic response of moderately thick laminated beams with damage

Thick beam theory and finite element model with zig-zag sublaminate approximations

First-order zig-zag sublaminate plate theory and finite element model for laminated composite and sandwich panels

Development of simple, robust finite elements based on refined theories for thick laminated beams

An improved theory and finite-element model for laminated composite and sandwich beams using first-order zig-zag sublaminate approximations