An adaptive nonlinear genetic algorithm for numerical optimization

Through the mechanism analysis of simple genetic algorithm (SGA), we find that every genetic operator can be considered as a linear transform. So some disadvantages of SGA may be solved if genetic operators are modified to a nonlinear transform. According to the above method, a nonlinear genetic algorithm is introduced, and different nonlinear genetic operators with some probabilities are designed and applied to numerical optimization problems. The optimization computing of some examples is made to show that the new genetic algorithm, is useful and simple.