SCL Sfit Genetic Commands

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gen_par {...} : Define parameters for controlling performance of genetic algorithm

When using method genetic, several parameters are used to control the performance of the Genetic Algorithm search procedure. A short explanation of the input parameters is given:

Parameter	Meaning	Default Value	Allowed Range
generations	The maximum number of generations the program will go through, before stopping, if the solution is not within the given tolerance.	10
population	The number of parents randomly generated in the first generation, and in each subsequent generation.	50
prob_mut	The probability of mutation in the offspring.	0.02	0 - 1
prob_cross	The probability of crossover between two parents.	0.8	0 - 1
tolerance	The highest suitability which will be tolerated.	1.0
selection_method	There are three selection methods available: tournament, rank, and fitness proportionate.	rank	rank tournament fitness
contestants	specific to tournament selection. This number of parents will be selected at random and from these the two with the lowest chi-squared values will be chosen as a pair of parents.	10
substring_length	defines the number of characters coding each parameter. This is multiplied by the number of parameters being varied to give the total string length for each parent.	16
alphabet _size	the number of different characters encoding the parent. For instance if this variable is given the value 2 the parent will be encoded by a string of ones and twos	3
elitism	an option which, if set to 'yes', always keeps the best few members of each generation.	no	yes no
kept	Only required if elitism is selected. Defines the number of individuals to be carried over to the next generation (if elitism is selected).	3

Example:

gen_par{
generations 10
population 50
prob_cross 0.8
prob_mut 0.02
tolerance 1.0
contestants 3
substring_length 16
alphabet_size 3
selection_method rank
elitism no
kept 2
}