GENETIC ALGORITHM APPROACH TO A LUMBER CUTTING OPTIMIZATION PROBLEM

被引：5

作者：

COOK, DF

WOLFE, ML

机构：

[1] Assistant Research Scientist Institute for Manufacturing Systems, Texas Engineering Experiment Station, TX, 77843, College Station

[2] Agricultural Engineering Department, Texas AM University Texas Agricultural Experiment Station, TX, 77843, College Station

来源：

CYBERNETICS AND SYSTEMS | 1991年 / 22卷 / 03期

关键词：

D O I：

10.1080/01969729108902288

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Genetic algorithms are a technique for search and optimization based on the Darwinian principle of natural selection. They are iterative search procedures that maintain a population of candidate solutions. The best or most fit solutions in that population are then used as the basis for the next generation of solutions. The next generation is formed using the genetic operators reproduction, crossover, and mutation. Genetic algorithms have been successfully applied to engineering search and optimization problems. This paper presents a discussion of the basic theory of genetic algorithms and presents a genetic algorithm solution of a lumber cutting optimization problem. Dimensional lumber is assigned a grade that represents its physical properties. A grade is assigned to every board segment of a specific length. The board is then cut in various locations in order to maximize its value. A genetic algorithm was used to determine the cutting patterns that would maximize the board value.

引用

页码：357 / 365

页数：9

共 6 条

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[3] Goldberg D.E., Computer-aided pipeline operation using genetic algorithms and rule learning. Part I: Genetic algorithms in pipeline optimization, Eng. Comp, 3, pp. 35-45, (1987)
[4] Grefenstette J.J., A user’s Guide to GENESIS. Navy Research Center for Applied Artificial Intelligence, Naval Research Laboratory, Washington, DC. Holland, J. H, (1987)
[5] Holland J.H., Outline for a logical theory of adaptive systems, Assoc. Comp. Mach. J, 9, 3, pp. 297-314, (1962)
[6] Grading Rules. SPIB, Pensacola, Florida. Walbridge, C. T, Genetic Algorithms: What Computers Can Learn from Darwin, Tech. Rev, 92, 1, pp. 47-53, (1977)

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