@article {pub4612pub4745,
	title = {Transfer Learning Based Co-surrogate Assisted
Evolutionary Bi-objective Optimization for
Objectives with Non-uniform Evaluation Times},
	author = {Xilu Wang AND Yaochu Jin AND Sebastian Schmitt AND Markus Olhofer},
	year = {2021},
	abstract = {Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that
each objective function can be evaluated within the same period of time, which is un-
tenable in many real-world optimization scenarios where evaluation of different ob-
jectives involves different computer simulations or physical experiments with distinct
time complexity. To address this issue, a transfer learning scheme based on surrogate-
assisted evolutionary algorithms (SAEAs) is proposed, in which a co-surrogate is
adopted to model the functional relationship between the fast and slow objective
functions and a transferable instance selection method is introduced to acquire use-
ful knowledge from the search process of the fast objective. Our experimental results
on DTLZ and UF test suites demonstrate that the proposed algorithm is competitive
for solving bi-objective optimization where objectives have non-uniform evaluation
times.},
	publisher = {MIT Press},
	journal = {Evolutionary Computation journal (ECJ)}
}