The original work in response surface methodology (RSM) has been widely used in the chemical and process industries. Recent years have seen more widespread use and new developments in RSM. RMS activities since 1989 are reviewed, and areas of current and..

Response Surface Methodology: A Retrospective and Literature Survey

Least squares polynomial chaos expansion: A review of sampling strategies

Computer-generated designs are useful for situations where standard factorial, fractional factorial or response surface designs cannot be easily employed. Alphabetically-optimal designs are the most widely used type of computer-generated designs, and of these, the D-optimal (or D-efficient) class of designs is extremely popular. D-optimal designs are usually constructed by algorithms that sequentially add and delete points from a potential design by using a candidate set of points spaced over the region of interest. We present a technique to generate D-efficient designs using genetic algorithms (GA). This approach eliminates the need to explicitly consider a candidate set of experimental points and it can be used in highly constrained regions while maintaining a level of performance comparable to more traditional design construction techniques.

Genetic Algorithms for the Construction of D-Optimal Designs

A technique for the construction of model-robust alphabetically-optimal designs using genetic algorithms is proposed. The method is particularly useful in mixture experiments and response surface experiments in constrained regions where computer-generat..

Model-Robust Optimal Designs: A Genetic Algorithm Approach

Genetic algorithms (GAs) have been used in many disciplines to optimize solutions for a broad range of problems. In the last 20 years, the statistical literature has seen an increase in the use and study of this optimization algorithm for generating optimal designs in a diverse set of experimental settings. These efforts are due in part to an interest in implementing a novel methodology as well as the hope that careful application of elements of the GA framework to the unique aspects of a designed experiment problem might lead to an efficient means of finding improved or optimal designs. In this paper, we explore the merits of using this approach, some of the aspects of design that make it a unique application relative to other optimization scenarios, and discuss elements which should be considered for an effective implementation. We conclude that the current GA implementations can, but do not always, provide a competitive methodology to produce substantial gains over standard optimal design strategies. We consider both the probability of finding a globally optimal design as well as the computational efficiency of this approach. Copyright © 2014 John Wiley & Sons, Ltd.

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Using Genetic Algorithms to Design Experiments: A Review

This paper studies the application of genetic algorithms to the construction of exact D-optimal experimental designs. The concept of genetic algorithms is introduced in the general context of the problem of finding optimal designs. The algorithm is then applied specifically to finding exact D-optimal designs for three different types of model. The performance of genetic algorithms is compared with that of the modified Fedorov algorithm in terms of computing time and relative efficiency. Finally, potential applications of genetic algorithms to other optimality criteria and to other types of model are discussed, along with some open problems for possible future research.

Grace Montepiedra

Papers

Application of genetic algorithms to the construction of exact D-optimal designs