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tutorial:heuristic_calibration_of_models_by_using_genetic_algorithm [2013/08/14 20:14] admin |
tutorial:heuristic_calibration_of_models_by_using_genetic_algorithm [2013/08/14 20:15] 127.0.0.1 external edit |
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| First, load the model ''Cal_Reciprocal_fitness1x1.ego'' from ''\ Examples\ Genetic_Algorithm\WEofE''. This model calculates the reciprocal fitness of a deforestation model that was calibrated using the Weights of Evidence method – a soft predictor. Run the model and access its output ''Reciprocal_fitness1x1.csv'' using either a spreadsheet or enabling table viewer on the output port of Set Key 1. The concept of this validation measure is provided in sixth and seventh chapters of lesson [[tutorial:building_a_land-use_and_land-cover_change_simulation_model|Building a Land use and Land-cover Change Simulation Model]]. The fitness obtained for this model is 0.2060. Open the Weights of Evidence tables in ''originals\tables'' and their corresponding maps in ''originals\maps'' under the folder ''Genetic_Algorithm''. In this model, //[[:Calculate Map]]// replaces //[[:calc_w._of_e._probability_map|Calc W. E. Probability Map]]//. Open it to see the equation that integrates the weights of evidence to produce the transition probability map. The weights of evidence coefficients are input as separate tables, so they can form a group of table and thereby the gene. | First, load the model ''Cal_Reciprocal_fitness1x1.ego'' from ''\ Examples\ Genetic_Algorithm\WEofE''. This model calculates the reciprocal fitness of a deforestation model that was calibrated using the Weights of Evidence method – a soft predictor. Run the model and access its output ''Reciprocal_fitness1x1.csv'' using either a spreadsheet or enabling table viewer on the output port of Set Key 1. The concept of this validation measure is provided in sixth and seventh chapters of lesson [[tutorial:building_a_land-use_and_land-cover_change_simulation_model|Building a Land use and Land-cover Change Simulation Model]]. The fitness obtained for this model is 0.2060. Open the Weights of Evidence tables in ''originals\tables'' and their corresponding maps in ''originals\maps'' under the folder ''Genetic_Algorithm''. In this model, //[[:Calculate Map]]// replaces //[[:calc_w._of_e._probability_map|Calc W. E. Probability Map]]//. Open it to see the equation that integrates the weights of evidence to produce the transition probability map. The weights of evidence coefficients are input as separate tables, so they can form a group of table and thereby the gene. | ||
| - | Now, open ''GAReciprocal_fitness1x1.ego'' from ''Genetic_Algorithm\GAknn\Reciprocal_fitness1x1''. Compare the structure of this model with the diagram from Fig.1. Open GA tool. This container envelops three //[[:Groups]]// and two functors. //[[:Get Current Individual]]// obtains the gene of an individual pertaining to a generation and passes it to a sequence of functors that extract the lookup tables that compose the gene. A land change simulation model receives those tables as input and its execution results are passed to a fitness function that assesses its spatial performance. In turn, this function returns the fitness measure that is caught and passed to GA tool by //[[:Set Fitness]]//. | + | Now, open ''GAReciprocal_fitness1x1.ego'' from ''Genetic_Algorithm\GAknn\Reciprocal_fitness1x1''. Compare the structure of this model with the diagram from Fig.1. Open GA tool. This container envelops three //[[:Group]]//s and two functors. //[[:Get Current Individual]]// obtains the gene of an individual pertaining to a generation and passes it to a sequence of functors that extract the lookup tables that compose the gene. A land change simulation model receives those tables as input and its execution results are passed to a fitness function that assesses its spatial performance. In turn, this function returns the fitness measure that is caught and passed to GA tool by //[[:Set Fitness]]//. |
| {{ :tutorial:ga_2.jpg |}} | {{ :tutorial:ga_2.jpg |}} | ||