Genetic algorithms for the construction of D-optimal designs

Alejandro Heredia-Langner, W. M. Carlyle, Douglas Montgomery, C. M. Borror, George Runger

Research output: Contribution to journalArticle

62 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)28-46
Number of pages19
JournalJournal of Quality Technology
Volume35
Issue number1
StatePublished - Jan 2003

Fingerprint

D-optimal Design
Genetic algorithms
Genetic Algorithm
Response Surface Design
Fractional Factorial
D-optimal
Factorial
Region of Interest
Set of points
Design
Optimal design
Genetic algorithm
Eliminate

Keywords

  • Computer-aided design
  • D-optimality
  • Design of experiments
  • Design optimality

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Statistics and Probability
  • Management Science and Operations Research

Cite this

Genetic algorithms for the construction of D-optimal designs. / Heredia-Langner, Alejandro; Carlyle, W. M.; Montgomery, Douglas; Borror, C. M.; Runger, George.

In: Journal of Quality Technology, Vol. 35, No. 1, 01.2003, p. 28-46.

Research output: Contribution to journalArticle

Heredia-Langner, A, Carlyle, WM, Montgomery, D, Borror, CM & Runger, G 2003, 'Genetic algorithms for the construction of D-optimal designs', Journal of Quality Technology, vol. 35, no. 1, pp. 28-46.
Heredia-Langner, Alejandro ; Carlyle, W. M. ; Montgomery, Douglas ; Borror, C. M. ; Runger, George. / Genetic algorithms for the construction of D-optimal designs. In: Journal of Quality Technology. 2003 ; Vol. 35, No. 1. pp. 28-46.
@article{890b4d498f4444bfaf9323d55f7391fe,
title = "Genetic algorithms for the construction of D-optimal designs",
abstract = "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.",
keywords = "Computer-aided design, D-optimality, Design of experiments, Design optimality",
author = "Alejandro Heredia-Langner and Carlyle, {W. M.} and Douglas Montgomery and Borror, {C. M.} and George Runger",
year = "2003",
month = "1",
language = "English (US)",
volume = "35",
pages = "28--46",
journal = "Journal of Quality Technology",
issn = "0022-4065",
publisher = "American Society for Quality",
number = "1",

}

TY - JOUR

T1 - Genetic algorithms for the construction of D-optimal designs

AU - Heredia-Langner, Alejandro

AU - Carlyle, W. M.

AU - Montgomery, Douglas

AU - Borror, C. M.

AU - Runger, George

PY - 2003/1

Y1 - 2003/1

N2 - 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.

AB - 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.

KW - Computer-aided design

KW - D-optimality

KW - Design of experiments

KW - Design optimality

UR - http://www.scopus.com/inward/record.url?scp=1042292395&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=1042292395&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:1042292395

VL - 35

SP - 28

EP - 46

JO - Journal of Quality Technology

JF - Journal of Quality Technology

SN - 0022-4065

IS - 1

ER -