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 ======  Heuristic calibration of models by using Genetic Algorithm  ======
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-The //[[:Genetic Algorithm Tool|Genetic Algorithm (GA) Tool]]// provides a powerful means of calibrating environmental models[[http://dx.doi.org/10.1016/j.envsoft.2013.01.010|( Soares Filho et al., 2013)]]. By mimicking the principle of biological evolution (Koza, 1992), GA tool uses massive computing and heuristics to seek for a global optimum solution for a set of model parameters. Dinamica EGO´s GA tool consists of a container, which requires the placement of a sequence of functors within it and, in particular, of two associated functors: //[[:Get Current Individual]]// and //[[:Set Fitness]]//. Fig. 1 depicts a model calibration scheme using GA tool. First, one needs to get the coefficients from the model parameters to be calibrated and assemble these coefficients in tables. Thus each model parameter will represent an allele in a table that corresponds to a gene. In turn, these tables are assembled by using //[[:Create Lookup Table]]// Group in a group of tables to form a chromosome. This group of tables is an input to GA tool. GA tool spawns a population based on the genotype passed in a group table. Inside GA tool, //[[:Get Current Individual]]// is placed to get the genes from the individuals of a generation. Other functors, such as //[[:Extract Lookup Table from Lookup Table Group]]//, are sequenced to catch the parameter coefficients and pass them to the model, which is executed once per individual. An evaluation function is coupled to the output of the model and its result is passed to //[[:Set Fitness]]//, which returns the fitness value to GA tool for the selection process. The internal sequence of functors will iterate a number of times equal to the number of individuals multiplied by generations, as specified in GA tool´s input ports. When GA tool terminates, it will output the fitness of the overall best individual as well as the group of tables that comprises its genes.
+The //[[:Genetic Algorithm Tool|Genetic Algorithm (GA) Tool]]// provides a powerful means of calibrating environmental models[[http://dx.doi.org/10.1016/j.envsoft.2013.01.010|( Soares Filho et al., 2013)]]. By mimicking the principle of biological evolution (Koza, 1992), GA tool uses massive computing and heuristics to seek for a global optimum solution for a set of model parameters. Dinamica EGO´s GA tool consists of a container, which requires the placement of a sequence of functors within it and, in particular, of two associated functors: //[[:Get Current Individual]]// and //[[:Set Fitness]]//. Fig. 1 depicts a model calibration scheme using GA tool. First, one needs to get the coefficients from the model parameters to be calibrated and assemble these coefficients in tables. Thus each model parameter will represent an allele in a table that corresponds to a gene.
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+In turn, these tables are assembled by using //[[:Create Lookup Table]]// Group in a group of tables to form a chromosome. This group of tables is an input to GA tool. GA tool spawns a population based on the genotype passed in a group table. Inside GA tool, //[[:Get Current Individual]]// is placed to get the genes from the individuals of a generation. Other functors, such as //[[:Extract Lookup Table from Lookup Table Group]]//, are sequenced to catch the parameter coefficients and pass them to the model, which is executed once per individual. An evaluation function is coupled to the output of the model and its result is passed to //[[:Set Fitness]]//, which returns the fitness value to GA tool for the selection process. The internal sequence of functors will iterate a number of times equal to the number of individuals multiplied by generations, as specified in GA tool´s input ports. When GA tool terminates, it will output the fitness of the overall best individual as well as the group of tables that comprises its genes.