Abstract
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.
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