## SCL Sfit Genetic Commands

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:

ParameterMeaningDefault ValueAllowed Rangegenerations The maximum number of generations the program will go through, before stopping, if the solution is not within the given tolerance. 10population The number of parents randomly generated in the first generation, and in each subsequent generation. 50prob_mut The probability of mutation in the offspring. 0.02 0 - 1prob_cross The probability of crossover between two parents. 0.8 0 - 1tolerance The highest suitability which will be tolerated. 1.0selection_method There are three selection methods available: tournament, rank, and fitness proportionate. rank rank

tournament

fitnesscontestants 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. 10substring_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. 16alphabet _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 3elitism an option which, if set to 'yes', always keeps the best few members of each generation. no yes

nokept Only required if elitism is selected. Defines the number of individuals to be carried over to the next generation (if elitism is selected). 3Example:

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

}