ssGA is a simple and easy to use implementation of a Steady State Generic Algorithm. This algorithm generates a new individual in every step. The new individual is inserted in the population with his parents, and he always replaces to the worst individual (or he only replaces if he is better than the worst).

[ssGA] // Steady State Genetic Algorithm

proc Rep_Cicle (ga)
      for s<-1 to MAX_STEPS do
            parent1<- Select (ga.population);
            parent2<- Select (ga.population);
            Cross (ga.Pc,parent1,parent2,indiv_aux.chromosome);
            Mutate (ga.Pm,indiv_aux.chromosome);
            indiv_aux.fitness<-ga.Evaluate(Decode(indiv_aux.chromosome));
            Insert_New_Indiv (ga,indiv_aux, [if better | if worse]);
            Makes_Stadisticals (ga);
      end_for
end_proc Rep_Cicle;

 

 


J. Cabello Galisteo 2008