Evolutionary 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 Oyama
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| Autore: |
Singh Hemant
|
| Titolo: |
Evolutionary 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 Oyama
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| Pubblicazione: | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
| Edizione: | 1st ed. 2025. |
| Descrizione fisica: | 1 online resource (397 pages) |
| Disciplina: | 006.3 |
| Soggetto topico: | Artificial intelligence |
| Artificial Intelligence | |
| Altri autori: |
RayTapabrata
KnowlesJoshua
LiXiaodong
BrankeJuergen
WangBing
OyamaAkira
|
| Nota di contenuto: | -- 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. |
| Sommario/riassunto: | 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. |
| Titolo autorizzato: | Evolutionary Multi-Criterion Optimization |
| ISBN: | 9789819635382 |
| 9819635381 | |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 996647866403316 |
| Lo trovi qui: | Univ. di Salerno |
| Opac: | Controlla la disponibilità qui |
Serie:
Lecture Notes in Computer Science, . 1611-3349 ; ; 15513