  "Innovization" is a task of learning common relationships among some or all
of the Pareto-optimal (PO) solutions in multi- and many-objective optimization
problems. Recent studies have shown that a chronological sequence of
non-dominated solutions obtained in consecutive iterations during an
optimization run also possess salient patterns that can be used to learn
problem features to help create new and improved solutions. In this paper, we
propose a machine-learning- (ML-) assisted modelling approach that learns the
modifications in design variables needed to advance population members towards
the Pareto-optimal set. We then propose to use the resulting ML model as an
additional innovized repair (IR2) operator to be applied on offspring solutions
created by the usual genetic operators, as a novel mean of improving their
convergence properties. In this paper, the well-known random forest (RF) method
is used as the ML model and is integrated with various evolutionary multi- and
many-objective optimization algorithms, including NSGA-II, NSGA-III, and
MOEA/D. On several test problems ranging from two to five objectives, we
demonstrate improvement in convergence behaviour using the proposed IR2-RF
operator. Since the operator does not demand any additional solution
evaluations, instead using the history of gradual and progressive improvements
in solutions over generations, the proposed ML-based optimization opens up a
new direction of optimization algorithm development with advances in AI and ML
approaches.
