Illinois Genetic Algorithms Laboratory

Sastry, K., Goldberg, D. E. (2004)
TR No.: 2004006 | Download PDF | Download PS

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 O(2^km^1.5), and
requires O(k^½logm) less function evaluations
than its selectorecombinative counterpart, confirming theoretical
results reported elsewhere (Sastry & Goldberg, 2004).