  We present an algorithm for multi-objective optimization of computationally
expensive problems. The proposed algorithm is based on solving a set of
surrogate problems defined by models of the real one, so that only solutions
estimated to be approximately Pareto-optimal are evaluated using the real
expensive functions. Aside of the search for solutions, our algorithm also
performs a meta-search for optimal surrogate models and navigation strategies
for the optimization landscape, therefore adapting the search strategy for
solutions to the problem as new information about it is obtained. The
competitiveness of our approach is demonstrated by an experimental comparison
with one state-of-the-art surrogate-assisted evolutionary algorithm on a set of
benchmark problems.
