A Many-objective Evolutionary Algorithm With Pareto-adaptive Reference Points−Supplementary Data: Part 1

1. The raw IGD and HV results are obtained by setting the number of maximum function evaluations to the values as described in Section III-A of the main paper.

2. The best and the second best results on each test problem are shaded with a dark and a light gray background, respectively.

3. In each table, the symbol $``\bullet"$ indicates that the proposed PaRP/EA significantly outperforms the peer algorithms at a 0.05 level by the Wilcoxon's rank sum test, whereas  $``\circ"$ indicates the opposite, i.e. the peer algorithm shows a significant improvement over PaRP/EA. If no significant difference is detected, it will be marked by the symbol $``\ddagger"$.