Integration of Evolutionary Algorithms and Machine Learning techniques in routing-related problems: A review

Abstract

This paper provides a systematic review of hybrid algorithms combining Evolutionary Algorithms (EAs) with Machine Learning (ML) techniques, focusing on routing-related problems.It explores the integration of various ML methods, such as Reinforcement Learning (RL), Supervised Learning (SL), and Unsupervised Learning (UL), with EAs, aiming to address complex, multi-objective optimization challenges often encountered in logistics, robotics, and network routing.The review identifies RL-based methods, particularly Q-Learning (QL) and Deep RL (DRL), as the most prominent approaches due to their adaptability and capability to dynamically adjust solutions.SL techniques, including Decision Trees (DTs) and Artificial Neural Networks (ANNs), and UL approaches, such as clustering, also play significant roles in enhancing EAs' performance.