A genetic algorithm with a Mendel operator for global minimization

Abstract

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.