Reinforcement Evolutionary Learning Method for self-learning

In statistical modelling the biggest threat is concept drift which makes the model gradually showing deteriorating performance over time. There are state of the art methodologies to detect the impact of concept drift, however general strategy considered to overcome the issue in performance is to rebuild or re-calibrate the model periodically as the variable patterns for the model changes significantly due to market change or consumer behavior change etc. Quantitative research is the most widely spread application of data science in Marketing or financial domain where applicability of state of the art reinforcement learning for auto-learning is less explored paradigm. Reinforcement learning is heavily dependent on having a simulated environment which is majorly available for gaming or online systems, to learn from the live feedback. However, there are some research happened on the area of online advertisement, pricing etc where due to the nature of the online learning environment scope of reinforcement learning is explored. Our proposed solution is a reinforcement learning based, true self-learning algorithm which can adapt to the data change or concept drift and auto learn and self-calibrate for the new patterns of the data solving the problem of concept drift. Index Terms-- Reinforcement learning, Genetic Algorithm, Q-learning, Classification modelling, CMA-ES, NES, Multi objective optimization, Concept drift, Population stability index, Incremental learning, F1-measure, Predictive Modelling, Self-learning, MCTS, AlphaGo, AlphaZero 1. Introduction Concept drift is well known challenge for sustainability of any machine learning predictive model over time. Machine learning offers diverse techniques to understand the underlying pattern of the data and associate the same with prediction objective. Any predictive modelling activity in either Marketing, Finance, Management are heavily dependent on the assumption that the training data represents the pattern of target population under specific study such as Fraud Identification, Customer churn prediction, Marketing mix modelling, Target customer identification for specific type of promotion etc. However due to social & economic development, customer behavior changes combined with other external factors making past learned pattern, irrelevant for current predictions.