A genetic algorithm search process acts much differently than simulated annealing or tabu search in how it attempts to solve a problem, as it employs information from several forest plans to search for the best overall plan. In employing a genetic algorithm search process, a population of feasible solutions is created and maintained, new members are developed by combining traits of current members of the population, and the overall population quality should improve as it evolves. The search process is analogous to the birth and death of a group of biological organisms. Potential new members of the population, child solutions, are provided genetic material (assignments of activities to decision variables) from current members of the population, or parent solutions. As the population evolves, at least one member should approach the maximum potential biological quality (the global optimum solution to a problem).