An improved genetic algorithm for a type of nonlinear programming problems

Based on the study on how to apply penalty strategy for solving a type of nonlinear programming problems by genetic algorithm, such conclusion can be drawn that only applying penalty strategy is inadequate to deal with nonlinear programming problems well. It is important to lead infeasible individuals into the feasible set during the evolution process. Penalty and repair strategy are associated to improve the performance of the algorithm. Based on such thought that the constraint which has the highest degree of violation can be satisfied first by enlarging the penalty on the individuals and repair, repair operator is proposed to perform repair operation of infeasible individuals. At the same time, based on optimization design theory, a method has been proposed to establish initial population by using uniform design. Thus, repair genetic algorithm (RGA) is proposed.