Eeciency Enhancement of Probabilistic Model Building Genetic Algorithms Eeciency Enhancement of Probabilistic Model Building Genetic Algorithms

This paper presents two diierent eeciency-enhancement techniques for probabilistic model building genetic algorithms. The rst technique proposes the use of a mutation operator which performs local search in the sub-solution neighborhood identiied through the probabilistic model. The second technique proposes building and using an internal probabilistic model of the tness along with the probabilistic model of variable interactions. The tness values of some oospring are estimated using the probabilistic model, thereby avoiding computationally expensive function evaluations. The scalability of the aforementioned techniques are analyzed using facetwise models for convergence time and population sizing. The speed-up obtained by each of the methods is predicted and veriied with empirical results. The results show that for additively separable problems the competent mutation operator requires O(p k log m)|where k is the building-block size, and m is the number of building blocks|less function evaluations than its selectorecombinative counterpart. The results also show that the use of an internal probabilistic tness model reduces the required number of function evaluations to as low as 1-10% and yields a speed-up of 2{50.