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https://doi.org/10.1007/978-981-99-2096-9Evolutionary Multi-objective Optimization; Machine Learning; Evolutionary Computation; Convergence; Dive
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Dhish Kumar Saxena,Sukrit Mittal,Erik D. GoodmanDedicated to machine learning based performance enhancements in evolutionary multi- and many objective optimization.Discusses the topics in a clear and structured manner, covering the search, post-opt
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,Learning to Diversify Better: IP3 Operator,It was emphasized earlier that evolutionary multi- and many-objective optimization algorithms, jointly referred to as EMâOAs, pursue the dual goals of convergence to and diversity across the true Pareto front (.). In that, diversity must be interpreted in terms of the extent of . (coverage of .) and . within a given spread.