Nonlinear mixed integer programming problems using genetic algorithm and penalty function

We propose a method for solving nonlinear mixed integer programming (NMIP) problems using genetic algorithms (GAs) and a penalty function method. The penalty function method was used to construct a fitness function to evaluate chromosomes generated from genetic reproduction. Therefore, the mean of satisfactory degrees of systems constraints were introduced. Also, we apply the method for solving optimization problems which belong to nonlinear programming or NMIP problems, using the proposed method. The performance of the proposed method was evaluated through numerical experiments to demonstrate the efficiency of the proposed method.