A new mutation operator for evolution strategies for constrained problems

We propose a new mutation operator - the biased mutation operator (BMO) -for evolution strategies, which is capable of handling problems for constrained fitness landscapes. The idea of our approach is to bias the mutation ellipsoid in relation to the parent and therefore lead the mutations into a beneficial direction self-adaptively. This helps to improve the success rate to reproduce better offspring. Experimental results show this bias enhances the solution quality within constrained search domains. The number of the additional strategy parameters used in our approach equals to the number of dimensions of the problem. Compared to the correlated mutation, the BMO needs much less memory and supersedes the computation of the rotation matrix of the correlated mutation and the asymmetric probability density function of the directed mutation.