Factorial design technique applied to genetic algorithm parameters in a batch cooling crystallization optimisation

An original approach is proposed in this work for the evaluation of genetic algorithm (GA) applied to a batch cooling crystallization optimisation. Since a lot of parameters must be set in a GA in order to perform an optimisation study, factorial design, a well-known technique for the selection of the variables with the most meaningful effects on a response, is applied in an optimisation problem solved through GA. No systematic approach to establish the best set of parameters for GA was found in literature and a relatively easy to use and meaningful approach is proposed. The results show that the parameters with significant (95% confidence) effect are initial population, the population size and the jump and creep mutation probabilities, being the ones in which alterations should be made during a GA study of optimisation, in the search for the optimum.