04285nam 22005775 450 99664786640331620250228120733.09789819635382981963538110.1007/978-981-96-3538-2(CKB)37726368400041(MiAaPQ)EBC31927445(Au-PeEL)EBL31927445(DE-He213)978-981-96-3538-2(OCoLC)1505735007(EXLCZ)993772636840004120250228d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierEvolutionary Multi-Criterion Optimization 13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4–7, 2025, Proceedings, Part II /edited by Hemant Singh, Tapabrata Ray, Joshua Knowles, Xiaodong Li, Juergen Branke, Bing Wang, Akira Oyama1st ed. 2025.Singapore :Springer Nature Singapore :Imprint: Springer,2025.1 online resource (397 pages)Lecture Notes in Computer Science,1611-3349 ;155139789819635375 9819635373 -- 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.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 and selected from 63 submissions. The papers are divided into the following topical sections: Part I : Algorithm design; Benchmarking; Applications. Part II : Algorithm analysis; Surrogates and machine learning; Multi-criteria decision support.Lecture Notes in Computer Science,1611-3349 ;15513Artificial intelligenceArtificial IntelligenceArtificial intelligence.Artificial Intelligence.006.3Singh Hemant1790150Ray Tapabrata1763711Knowles Joshua1790151Li Xiaodong1790152Branke Juergen1790153Wang Bing1217580Oyama Akira1790154MiAaPQMiAaPQMiAaPQBOOK996647866403316Evolutionary Multi-Criterion Optimization4326244UNISA