A Study on Many-Objective Optimization Using the Kriging-Surrogate-Based Evolutionary Algorithm Maximizing Expected Hypervolume Improvement

Table 1

Parameter values used in NSGA-II.

Parameters	NSGA-II
Population size	100
Number of generations	200
Simulated binary crossover probability	1
Polynomial mutation probability	1/n
[33]	30
[33]	20