Necessary and Sufficient Conditions for Surrogate Functions of Pareto
  Frontiers and Their Synthesis Using Gaussian Processes
  This paper introduces the necessary and sufficient conditions that surrogate
functions must satisfy to properly define frontiers of non-dominated solutions
in multi-objective optimization problems. These new conditions work directly on
the objective space, thus being agnostic about how the solutions are evaluated.
Therefore, real objectives or user-designed objectives' surrogates are allowed,
opening the possibility of linking independent objective surrogates. To
illustrate the practical consequences of adopting the proposed conditions, we
use Gaussian processes as surrogates endowed with monotonicity soft constraints
and with an adjustable degree of flexibility, and compare them to regular
Gaussian processes and to a frontier surrogate method in the literature that is
the closest to the method proposed in this paper. Results show that the
necessary and sufficient conditions proposed here are finely managed by the
constrained Gaussian process, guiding to high-quality surrogates capable of
suitably synthesizing an approximation to the Pareto frontier in challenging
instances of multi-objective optimization, while an existing approach that does
not take the theory proposed in consideration defines surrogates which greatly
violate the conditions to describe a valid frontier.
