Optimisation algorithms for spatially constrained forest planning

We compared genetic algorithms, simulated annealing and hill climbing algorithms on spatially constrained, integrated forest planning problems. There has been growing interest in algorithms that mimic natural processes, such as genetic algorithms and simulated annealing. These algorithms use random moves to generate new solutions, and employ a probabilistic acceptance/rejection criterion that allows inferior moves within the search space. Algorithms for a genetic algorithm, simulated annealing, and random hill climbing are formulated and tested on a same-sample forest-planning problem where the adjacency rule is strictly enforced.