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Evolutionary Multi-Criterion Optimization 13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4–7, 2025, Proceedings, Part II /

This two-volume set LNCS 15512-15513 constitutes the proceedings of the 13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025, held in Canberra, ACT, Australia, in March 2025. The 38 full papers and 2 extended abstracts presented in this book were carefully reviewed an...

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محفوظ في:
التفاصيل البيبلوغرافية
مؤلف مشترك: SpringerLink (Online service)
مؤلفون آخرون: Singh, Hemant (المحرر), Ray, Tapabrata (المحرر), Knowles, Joshua (المحرر), Li, Xiaodong (المحرر), Branke, Juergen (المحرر), Wang, Bing (المحرر), Oyama, Akira (المحرر)
التنسيق: الكتروني كتاب الكتروني
اللغة:الإنجليزية
منشور في: Singapore : Springer Nature Singapore : Imprint: Springer, 2025.
الطبعة:1st ed. 2025.
سلاسل:Lecture Notes in Computer Science, 15513
الموضوعات:
Artificial intelligence.
Artificial Intelligence.
الوصول للمادة أونلاين:https://doi.org/10.1007/978-981-96-3538-2
الوسوم: إضافة وسم
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جدول المحتويات:
  • -- Algorithm analysis.
  • Visual Explanations of Some Problematic Search Behaviors of Frequently Used EMO Algorithms.
  • Numerical Analysis of Pareto Set Modeling.
  • When Is Non-deteriorating Population Update in MOEAs Beneficial?.
  • Analysis of Merge Non-dominated Sorting Algorithm.
  • Comparative Analysis of Indicators for Multi-objective Diversity Optimization.
  • Performance Analysis of Constrained Evolutionary Multi-Objective Optimization Algorithms on Artificial and Real-World Problems.
  • On the Approximation of the Entire Pareto Front of a Constrained Multi objective Optimization Problem.
  • Small Population Size is Enough in Many Cases with External Archives.
  • Surrogates and machine learning.
  • Knowledge Gradient for Multi-Objective Bayesian Optimization with Decoupled Evaluations.
  • Surrogate Strategies for Scalarisation-based Multi-objective Bayesian Optimizers.
  • A Mixed-Fidelity Evaluation Algorithm for Efficient Constrained Multi- and Many-Objective Optimization: First Results.
  • Efficient and Accurate Surrogate-Assisted Approach to Multi-Objective Optimization Using Deep Neural Networks.
  • Large Language Model for Multiobjective Evolutionary Optimization.
  • Multi-Objective Multi-Agent Reinforcement Learning for Autonomous Driving in Mixed-Traffic Environments.
  • Parallel TD3 for Policy Gradient-based Multi-Condition Multi-Objective Optimisation.
  • Multi-criteria decision support.
  • Reliability-based MCDM Using Objective Preferences Under Variable Uncertainty.
  • An Efficient Iterative Approach for Uniformly Representing Pareto Fronts.
  • Preference Learning for Multi-objective Reinforcement Learning by Means of Supervised Learning.
  • Bayesian preference elicitation for decision support in multi-objective optimization.

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