Analysis of Asymmetric Mutation Model in Random Local Search
Hiroshi Furutani, Makoto Sakamoto, Yifei Du, Kenji Aoki · Journal of Robotics Networking and Artificial Life · 2015
In a standard Evolutionary Algorithms (EAs), one uses the same rate for mutations from bit 1 to bit 0 and its reverse direction.There are many reports that the asymmetric mutation model is a very powerful strategy in EAs to obtain better solutions more efficiently.In this paper, we report stochastic behaviors of algorithms that are asymmetric mutation models of Random Local Search (RLS).The mathematical structure of asymmetry model can be derived in terms of a finite Markov chain.We demonstrate some useful results representing the effects of asymmetric mutation.