There are great numbers of evolutionary algorithms for optimization of engineering problems which each of them gives its own objective function value in a defined problem. Using the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms is proposed by researchers for solving optimization problems.
- What are the rationales behind using the q-Gaussian mutation operators in evolutionary algorithms?
- What advantage it has in dealing with engineering optimization problems?
- Can it be used and applied to some other meta-heuristic algorithms?
