Mechanism of Multi-Objective Genetic Algorithm for Maintaining the Solution Diversity Using Neural Network

When multi-objective genetic algorithm is applied to real world problems for deriving Pareto optimum solutions, high calculation cost becomes a problem. One of solutions of this problem is using small number of population size. With this solution, however, it often happens that the diversity of the solutions is lost. Then the solutions which have the sufficient precisions cannot be derived. For overcoming this difficulty, the solutions should be re-placed when the solutions are converged on a certain point. To perform this re-placement, inverse analyze to derive the design variables from objects since the solutions are located in the objective space. For this purpose, in this paper, the Artificial Neural Network (ANN) is applied. Using ANN, the solutions which are concentrated on certain points are re-placed and the diversity of the solutions is maintained. In this paper, the new mechanism using ANN to keep the diversity of the solutions is proposed. The proposed mechanism is introduced into NSGA-II and applied for the test functions. It is discussed that in some functions the proposed mechanism is useful compared to the conventional method. In other numerical experiments, the results of the proposed algorithm with plentifully population are discussed and the affection of the proposed mechanism is also described.