Foundational Studies on ML-Based Enhancements

Many efficient evolutionary multi- and many-objective optimization algorithms, jointly referred to as EMâOAs, have been proposed in the last three decades. However, while solving complex real-world problems, EMâOAs that rely only on natural variation and selection operators may not produce an efficient search [14, 33, 45]. Therefore, it may be desirable or essential to enhance the capabilities of EMâOAs by introducing synergistic concepts from probability, statistics, machine learning (ML), etc. This chapter highlights some of the key studies that have laid the foundations for ML-based enhancements for EMâOAs and inspired further research that has been shared in subsequent chapters.