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		<title>Ensemble Machine Learning Helps Evolutionary Algorithms Track Changing Optimization Targets</title>
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		<pubDate>Sun, 25 Oct 2026 15:31:43 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[CEC 2018 benchmark]]></category>
		<category><![CDATA[Cluster Computing]]></category>
		<category><![CDATA[dynamic multi-objective optimization]]></category>
		<category><![CDATA[ensemble learning]]></category>
		<category><![CDATA[environmental response mechanism]]></category>
		<category><![CDATA[evolutionary computation]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Pareto front prediction]]></category>
		<category><![CDATA[population reinitialization]]></category>
		<category><![CDATA[Random Forest]]></category>
		<category><![CDATA[support vector regression]]></category>
		<category><![CDATA[XGBoost]]></category>
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					<description><![CDATA[A new ensemble machine learning-based prediction mechanism reinitializes evolutionary algorithm populations after environmental changes and achieved the best MIGD or MHVD values on many CEC 2018 dynamic multi-objective benchmark problems compared with eight state-of-the-art response mechanisms.]]></description>
		
		
		
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