The performance of a newly designed evolutionary algorithm is usually evaluated by computational experiments in comparison with existing algorithms. However, comparison results depend on experimental setting; thus, fair comparison is difficult. Fair comparison of multi-objective evolutionary algorithms is even more difficult since solution sets instead of solutions are evaluated. In this paper, the following four issues are discussed for fair comparison of multi-objective evolutionary algorithms: (i) termination condition, (ii) population size, (iii) performance indicators, and (iv) test problems. Whereas many other issues related to computational experiments such as the choice of a crossover operator and the specification of its probability can be discussed for each algorithm separately, all the above four issues should be addressed for all algorithms simultaneously. For each issue, its strong effects on comparison results are first clearly demonstrated. Then, the handling of each issue for fair comparison is discussed. Finally, future research topics related to each issue are suggested.

中文翻译：

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多目标进化算法公平性能比较的难点【研究前沿】


新设计的进化算法的性能通常通过计算实验与现有算法进行比较来评估。然而，比较结果取决于实验设置；因此，公平比较是困难的。多目标进化算法的公平比较甚至更加困难，因为评估的是解决方案集而不是解决方案。在本文中，为了公平比较多目标进化算法，讨论了以下四个问题：（i）终止条件，（ii）种群规模，（iii）性能指标和（iv）测试问题。尽管与计算实验相关的许多其他问题（例如交叉算子的选择及其概率的指定）可以针对每个算法单独讨论，但所有上述四个问题都应该针对所有算法同时解决。对于每个问题，首先清楚地证明其对比较结果的强烈影响。然后，讨论了每个问题的处理以进行公平比较。最后，提出了与每个问题相关的未来研究主题。