Enhancing Random Forest Using Genetic Algorithm for Lifelong Machine Learning

Abstract

Learning over time for machine learning (ML) models is emerging as a new field, often called continual learning or lifelong Machine learning (LML). Today, deep learning and neural networks are the prevalent approaches for LML models. However, they are often criticized for being "black box" methods, and catastrophic forgetting has remained a persistent challenge throughout their development. This paradigm represents a significant shift from traditional static learning models, enabling systems to adapt to new data continuously while retaining previously acquired knowledge. In this paper, an LML approach is proposed that combines a Random Forest (RF) with a Genetic Algorithm (GA) to transfer the knowledge from an existing RF model to a new learning model. Here, the GA is applied to the RF model so that the weights of this model get balanced more steadily. The approach is evaluated here for classification problems on three benchmark datasets. The initial results present knowledge retention in the new model, indicating the success of the model. These methods are regularization-based and effective in mitigating catastrophic forgetting. However, they rely on Fisher Information to estimate parameter importance or similar measures, which are computationally demanding and suitable for deep neural networks. While many NN-based lifelong learning approaches have been studied, RFs remain comparatively underexplored in this paradigm. Given their robustness, interpretability, and effectiveness in tabular datasets with limited samples, RFs present a compelling alternative. This motivates our work on developing an RF-based lifelong learning approach.

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