Research Article
Selection and Penalty Strategies for Genetic Algorithms Designed to Solve Spatial Forest Planning Problems
Algorithm 1
Pseudocode for the genetic algorithm, plus fitness and penalty evaluation functions.
The canonical genetic algorithm mimics evolution via three basic operations: selection,
recombination, and mutation. Fitness evaluation consists of calculating net present value
and then levying penalties. This algorithm displays the dynamic penalty
approach, which is modeled after strategic oscillation. For each constraint , the penalty coefficient is increased (decreased) according to a running tally on the number of
consecutive generations in which the population is infeasible (feasible). For the static
penalty approach, the penalty coefficient remains constant across generations.
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