|
|||
Designing Competent Mutation Operators Via Probabilistic Model Building of NeighborhoodsKumara Sastry1,2 and David E. Goldberg1,3 1Illinois Genetic Algorithms Laboratory (IlliGAL)
2Department of Material Science & Engineering
3Department of General Engineering, University of Illinois at Urbana-Champaign, Urbana IL 61801 Abstract. This paper presents a competent selectomutative genetic algorithm (GA), that adapts linkage and solves hard problems quickly, reliably, and accurately. A probabilistic model building process is used to automatically identify key building blocks (BBs) of the search problem. The mutation operator uses the probabilistic model of linkage groups to find the best among competing building blocks. The competent selectomutative GA successfully solves additively separable problems of bounded difficulty, requiring only subquadratic number of function evaluations. The results show that for additively separable problems the probabilistic model building BB-wise mutation scales as , and requires less function evaluations than its selectorecombinative counterpart, confirming theoretical results reported elsewhere [1]. LNCS 3103, p. 114 ff. lncs@springer.de
|