Optimal combination of conflicting evidence based on genetic algorithm

The Dempster-Shafer (D-S) evidence theory is widely used in many fields of information fusion. However, counter-intuitive results may be obtained by the classical Dempster combination rule when collected evidences are highly in conflict with each other. So an optimal weighted average information fusion method is proposed. Firstly, the global distance of the weighted evidence is presented based on the distance function. Secondly, the optimal weight of every evidence body was acquired by genetic algorithm, while the global distance is the minimum. Finally, the weighted average of the masses is combined by classical Dempster's rule. Compared with existing methods, the proposed optimal method can effectively handle conflicting evidence with better performance of convergence. Numerical examples are illustrated the effectiveness of the proposed method. ICIC International