Vai al contenuto principale della pagina

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



(Visualizza in formato marc)    (Visualizza in BIBFRAME)

Autore: Singh Hemant Visualizza persona
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 Visualizza cluster
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  Visualizza cluster
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