抄録
Multiple Coding Genetic Algorithm (MCGA) is proposed as a multi-objective GA method, in which each individual specializes on one of many objectives and has a different decoding according each objective. The proposed method is applied to 2-objective knapsack problems and 2-objective flowshop scheduling problem, and its performance is compared with the ordinal parallel selection. In the application to knapsack problems, the data sets with different correlation coefficients are generated in a systematic way to control the distribution of a Pareto set. Quantitative evaluations of the obtained solutions are given by relative accuracy, cover ratio, diversity and the number of acquired solutions. In the application to flowshop scheduling, heuristic decoding methods are proposed for each objective. Simulation result shows that the proposed method is effective to obtain diverse solutions especially in the problem with a large Pareto set, which results either from a statistical character of a given problem, or from a largeness of a problem scale, or from a high dimensionality of a solution space.
