| Input: : recombination probability, : mutation probability, : addition probability, : deletion probability, : maximum |
| length of solutions, : number of generations that is constant during these generations |
| (i) Initialization: , is an auxiliary variable, is a random initial population with the maximum length of solutions |
| is equal to |
| (ii) Evaluation: calculation of fitness value of each solution in the population |
| (iii) |
| (iv) If then and |
| (v) Parent Selection: randomly selection of parents from the population by a given parent selection method (e.g., roulette wheel) |
| (vi) Recombination: applying a recombination operator (e.g., 1-point crossover) on each pair of parents with the probability |
| of to generate new offspring |
| (vii) Mutation: applying a mutation operator (e.g., uniform mutation) on each offspring generated from the previous step with |
| the probability of |
| (viii) Addition: applying addition operator on each offspring generated from the previous step with the probability of while |
| the length of solutions must be less than or equal to |
| (ix) Deletion: applying deletion operator on each offspring generated from the previous step with the probability of while |
| the length of solutions must be greater than or equal to 1 |
| (x) Evaluation: calculation of fitness value of offspring |
| (xi) Survival Selection: randomly selection of the individuals from the current population and generated offspring by a given |
| survival selection method (e.g., fitness based or age based) |
| (xii) Termination Condition: if termination condition(s) is satisfied, the algorithm is finished; otherwise go to step (iii). |
| When the algorithm is finished, the best solution with the highest fitness value is the output of the algorithm. |
