Titlebook: Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization; Dhish Kumar Saxena,Sukrit Mittal,Erik D. Goodman Book 2024

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,Foundational Studies on ML-Based Enhancements,obability, 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.

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Learning to Converge Better: IP2 Operator, relationships can be used for offspring ., within the same EMâOA run, to help induce better convergence [7,8]. Any attempt to eliminate the a priori specification of the relationship structure would require alternative criteria that could guide the improvement in convergence.

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,Foundational Studies on ML-Based Enhancements,ades. 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 pr

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Learning to Converge Better: IP2 Operator,s can be extracted in any intermediate generation of an evolutionary multi- and many-objective optimization algorithm (EMâOA) run. Subsequently, these relationships can be used for offspring ., within the same EMâOA run, to help induce better convergence [7,8]. Any attempt to eliminate the a priori

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,Learning to Simultaneously Converge and Diversify Better: UIP Operator,based EMâOAs or RV-EMâOAs, pursue the dual goals of convergence-to and diversity-across the true Pareto front (.). In previous chapters, IP2 and IP3 operators have been discussed with a focus solely on convergence enhancement and diversity enhancement, respectively.

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