Efficiency Enhancement of Probabilistic Model Building Genetic Algorithms

Abstract. This paper presents two different efficiency-enhancement techniques for probabilistic model building genetic algorithms. The first technique proposes the use of a mutation operator which performs local search in the sub-solution neighborhood identified through the probabilistic model. The second technique proposes building and using an internal probabilistic model of the fitness along with the probabilistic model of variable interactions. The fitness values of some offspring 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 verified with empirical results. The results show that for additively separable problems the competent mutation operator requires O( k log m)--where k is the building-block size, and m is the number of buildi...

• Type: Journal
• Year: 2004
• Where: CORR
• Authors: Kumara Sastry, David E. Goldberg, Martin Pelikan
• Keywords: CORR 2004, Education, Internal Probabilistic Model, Mutation Operator, Probabilistic Model