由于无法得到准确的期望效用函数，在信息不完全和结果不确定的环境下作出决策是困难的。提出基于候选方案排序的进化决策方法。通常通过分析得出一组与候选方案期望效用相关的指标，设计决策规则归结为寻找二者之间的相关关系。如果将所有候选方案按其对效用有影响的指标分为n类，并利用进化算法在n!空间中搜索全部方案的期望效用排序，则根据此排序作出最佳决策。提出针对排序问题的遗传算法。该方法较少依赖专家知识，无须显式地构造期望效用函数，能有效处理非数值或非量化指标以及指标冲突和指标相关等问题，在带随机噪声环境下仍能获得稳健解。在仿真机器人控制器设计中的应用表明了该方法的有效性。

Since the exact Expected Utility Functions (EUF) are not available in many circumstances, it is hard to make decision in environment characterized by incomplete information and uncertain decision results. Being aware of the defects of traditional decision analysis techniques, a new decision making method named Evolutionary Decision Making Based on Candidates Ranking is proposed. By selecting a set of indexes relevant to the expected utility of the candidates, the construction of decision rules can be reduced to finding the quantitative relationship between them. If all of the candidates are classified according to the indexes relevant to their expected utility, then Evolutionary Algorithms can be used to search for the expected utility ranking of the whole set of candidate classes, thus the optimal decision can be made based on the ranking. Some special considerations for Genetic Algorithms for ordering problem are also highlighted. The new method enjoys the advantages of weak dependence on expert knowledge, robustness in environment with random noise, no dependence on explicit EUF, effectively treating non-numerical, non-quantificational indexes and conflicts or correlation among indexes. The effectiveness of the proposed method is validated by its successful application in the controller design of certain simulated robot.