A genetic algorithm with a Mendel operator for global minimization

This paper proposes a modified genetic algorithm for global minimization. The algorithm uses a new genetic operator, the Mendel operator. This algorithm finds one of the local minimizers first and then finds a lower minimizer at the next iteration as a tunneling algorithm or a filled function method. By repeating these processes, a global minimizer can finally be obtained. Mendel operations simulating Mendel´s genetic law are devised to avoid converging to the same minimizer of the previous run. Also, the proposed algorithm guarantees convergence to a lower minimizer by using an elitist method