Approximation-Guided Evolutionary Multi-Objective Optimization

Karl Bringmann, Tobias Friedrich, Frank Neumann, Markus Wagner

Multi-objective optimization problems arise frequently
in applications but can often only be solved
approximately by heuristic approaches. Evolutionary
algorithms have been widely used to tackle
multi-objective problems. These algorithms use
different measures to ensure diversity in the objective
space but are not guided by a formal notion
of approximation. We present a new framework of
an evolutionary algorithm for multi-objective optimization
that allows to work with a formal notion
of approximation. Our experimental results
show that our approach outperforms state-of-the-art
evolutionary algorithms in terms of the quality
of the approximation that is obtained, in particular
for problems with many objectives.