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Termination MethodsTermination is the criterion by which the
genetic algorithm decides whether to continue searching
or stop the search. Each of the enabled termination
criterion is checked after each generation to see if it
is time to stop. Evolution Time - A termination method that stops the evolution when the elapsed evolution time exceeds the user-specified max evolution time. By default, the evolution is not stopped until the evolution of the current generation has completed, but this behavior can be changed so that the evolution can be stopped within a generation. Fitness Threshold - A termination method that stops the evolution when the best fitness in the current population becomes less than the user-specified fitness threshold and the objective is set to minimize the fitness. This termination method also stops the evolution when the best fitness in the current population becomes greater than the user-specified fitness threshold when the objective is to maximize the fitness. Fitness Convergence -
A termination method that stops the evolution when the
fitness is deemed as converged. Two filters of different
lengths are used to smooth the best fitness across the
generations. When the smoothed best fitness from the long
filter is less than a user-specified percentage away from
the smoothed best fitness from the short filter, the
fitness is deemed as converged and the evolution
terminates. Gene Convergence - A termination method that stops the evolution when a user-specified percentage of the genes that make up a chromosome are deemed as converged. A gene is deemed as converged when the average value of that gene across all of the chromosomes in the current population is less than a user-specified percentage away from the maximum gene value across the chromosomes. HyperVolume Convergence - HyperVolume Convergence is a termination method for Multi-Objective Optimization in which the optimal solution set is measured by HyperVolume of the Pareto-Optimal Front. Evolution is stopped when the HyperVolume stabilizes. Greedy Search & Back Elimination - Greedy Search is a termination method for AttributeSelection. When the Greedy Search algorithm is used, the evolution terminates immediately when adding a single input to the previous input set does not improve the fitness. When Back Elimination is used, the evolution terminates when removing a single input from the previous input collection leads to a worse fitness. A variation of the Greedy Search and Back Elimination is also implemented that keeps an elite pool with the size defined as EliteSize. When all solutions in the elite pool are checked using above criteria, the search is terminated. |
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