Integrating Active Learning for Improved Preference Modeling in Tree-Based Interactive Evolutionary Multi-Objective Algorithms

Multi-objective optimization problems are characterized by conflicting objectives, making it impossible to identify a single optimal solution. Instead, solution methods aim to produce a diverse set of non-dominated solutions, aka Pareto optimal solutions, each offering different tradeoffs among the...

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Veröffentlicht in:	2025 IEEE Congress on Evolutionary Computation (CEC) S. 1 - 8
Hauptverfasser:	Shavarani, Seyed Mahdi, Golabi, Mahmoud, Idoumghar, Lhassane
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 08.06.2025
Schlagworte:	Accuracy Active learning Adaptation models Decision making Decision Tree Classification Decision trees Interactive Evolutionary Multi-Objective Algorithms Optimization Pareto optimization Preference Learning Radio frequency Random Forest Classification Random forests Uncertainty
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