@article {pub3062,
	title = {A Multiobjective Evolutionary Algorithm using Gaussian Process based Inverse Modeling},
	author = {Ran Cheng AND Yaochu Jin AND Kaname Narukawa},
	year = {2015},
	month = {October},
	abstract = {To approximate the Pareto front, most existing
multiobjective evolutionary algorithms store the non-dominated
solutions found so far in the population or in an external
archive during the search. Such algorithms often require a high
degree of diversity of the stored solutions and only a limited
number of solutions can be achieved. By contrast, model-based
algorithms can alleviate the requirement on solution diversity
and in principle, as many solutions as needed can be generated.
This paper proposes a new model-based method for representing
and searching non-dominated solutions. The main idea is to
construct Gaussian process based inverse models that map all
found non-dominated solutions from the objective space to the
decision space. These inverse models are then used to create
offspring by sampling the objective space. To facilitate inverse
modeling, the multivariate inverse function is decomposed into
a group of univariate functions, where the number of inverse
models is reduced using a random grouping technique. Extensive
empirical simulations demonstrate that the proposed algorithm
exhibits robust search performance on a variety of medium
to high dimensional multiobjective optimization test problems.
Additional non-dominated solutions are generated a posteriori
using the constructed models to increase the density of solutions
in the preferred regions at a low computational cost.},
	publisher = {IEEE Press},
	journal = {IEEE Transactions on Evolutionary Computation},
	volume = {19},
	number = {6},
	pages = {838-856}
}