Sukrit Mittal

Sanjiv R. Das, Harshad Khadilkar, Sukrit Mittal et al.

Applying concepts related to zero-shot meta-learning and pre-training of foundation models, we develop a meta reinforcement learning approach (denoted MetaRL) that is pre-trained on thousands of goals-based wealth management (GBWM) problems. Each GBWM problem involves a multiple year scenario over which the investor looks to optimally choose an investment portfolio each year and choose to fulfill all, some, or none of the different financial goals that arise each year. These choices seek to maximize the expected total investor utility obtained from the fulfilled financial goals. By eliminating separate training and optimization for each new investor problem, the MetaRL model in inference mode produces near-optimal dynamic investment portfolio and goal-fulfilling strategies for a new GBWM problem within a few hundredths of a second. This delivers expected utilities that are, on average, 97.8% of the optimal expected utilities (determined via Dynamic Programming). These results are remarkably robust to capital market regime changes, even when training uses only one capital market regime. Further, the MetaRL approach can enable solving problems with larger state spaces where Dynamic Programming becomes computationally infeasible.

Sukrit Mittal, Dhish Kumar Saxena, Kalyanmoy Deb et al.

"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.