Learning effective problem information from already explored search space in an optimization run, and utilizing it to improve the convergence of subsequent solutions, have represented important directions in Evolutionary Multi-objective Optimization (EMO) research. In this article, a machine learning (ML)-assisted approach is proposed that: (a)
maps
the solutions from earlier generations of an EMO run to the current non-dominated solutions
in the decision space
; (b) learns the salient patterns in the
mapping
using an ML method, here an artificial neural network (ANN); and (c) uses the learned ML model to
advance
some of the subsequent offspring solutions in an adaptive manner. Such a multi-pronged approach, quite different from the popular
surrogate-modeling
methods, leads to what is here referred to as the
Innovized Progress
(IP) operator. On several test and engineering problems involving two and three objectives, with and without constraints, it is shown that an EMO algorithm assisted by the IP operator offers faster convergence behavior, compared to its base version independent of the IP operator. The results are encouraging, pave a new path for the performance improvement of EMO algorithms, and set the motivation for further exploration on more challenging problems.
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