  In this paper, we investigate the possibility of improving the performance of
multi-objective optimization solution approaches using machine learning
techniques. Specifically, we focus on multi-objective binary linear programs
and employ one of the most effective and recently developed criterion space
search algorithms, the so-called KSA, during our study. This algorithm computes
all nondominated points of a problem with p objectives by searching on a
projected criterion space, i.e., a (p-1)-dimensional criterion apace. We
present an effective and fast learning approach to identify on which projected
space the KSA should work. We also present several generic features/variables
that can be used in machine learning techniques for identifying the best
projected space. Finally, we present an effective bi-objective optimization
based heuristic for selecting the best subset of the features to overcome the
issue of overfitting in learning. Through an extensive computational study over
2000 instances of tri-objective Knapsack and Assignment problems, we
demonstrate that an improvement of up to 12% in time can be achieved by the
proposed learning method compared to a random selection of the projected space.
