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--- genetic_algorithm_tool [2012/09/13 05:08]
hermann [Notes]
+++ genetic_algorithm_tool [2012/11/28 22:48]
127.0.0.1 external edit
@@ Line 37: / Line 37: @@
 ===== Notes =====
 To calibrate Weights of Evidence coefficients, the ".dcf" file representing the Weight of Evidence ranges and coefficients can be converted into the corresponding set of ".csv" tables and back to the original format using the [[weights_of_evidence_file_conversion|ConvertWeightFiles utility]].
+If the "Meta Heuristic Evaluation Percent" parameter is defined, a percent of individuals estimate their corresponding fitness value using the [[wp>K-nearest_neighbor_algorithm| KNN algorithm]]. To ensure the KNN estimations are accurate, the meta heuristic is used only after a given number of generations (usually after 30% of the total generations).
+When the convergence is used, it is detected when there is not even a minimum percent (1 - "Convergence Limit") of improvement in the best fitness value in the window formed by the "Number of Generations" last generations.
+Here is a summary of the algorithm implemented by this functor:
+) First, an initial population of "Population Size" individuals is generated using the initial individual ("Individual Genotype") as a seed and the given bounds as lower and upper limit ("Upper Bound" and "Lower Bound", respectively).
+) Basically, there are two different execution modes (controlled by the "Use Convergence Stopping Criteria" flag): fixed number of generations ("Number of Generations") or iteration until convergence.
+) The fitness value for all individuals is calculated and the individual having the best fitness value is saved internally as the best individual for the current generation.
+) A given percent of the individuals is selected based on [[wp>Tournament_selection|tournament]] results. The selected individuals are reproduced using [[wp>Crossover_(genetic_algorithm)|crossovers]] and [[wp>Mutation_(genetic_algorithm)|mutations]] and used to form the next generation.
+) If the stopping criteria defined by item 2 was not reached yet, the execution continues at item 3. Otherwise, the individual having the best fitness among all generations is returned.
 ===== Internal Name =====
 GeneticAlgorithmTool