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Evolutionary multi-criterion optimiz...

EMO (Conference) (2025 :)

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Evolutionary multi-criterion optimization = 13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4-7, 2025 : proceedings.. Part II /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Evolutionary multi-criterion optimization/ edited by Hemant Singh ... [et al.].
其他題名:	13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4-7, 2025 : proceedings.
其他題名:	EMO 2025
其他作者:	Singh, Hemant.
團體作者:	EMO (Conference)
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	xvii, 266 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Multiple criteria decision making - Congresses. -
電子資源:	https://doi.org/10.1007/978-981-96-3538-2
ISBN:	9789819635382

Evolutionary multi-criterion optimization = 13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4-7, 2025 : proceedings.. Part II /
Evolutionary multi-criterion optimization13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4-7, 2025 : proceedings.Part II /[electronic resource] :EMO 2025edited by Hemant Singh ... [et al.]. - Singapore :Springer Nature Singapore :2025. - xvii, 266 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,155131611-3349 ;. - Lecture notes in computer science ;15513..

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.

ISBN: 9789819635382

Standard No.: 10.1007/978-981-96-3538-2doiSubjects--Topical Terms:

694229
Multiple criteria decision making
--Congresses.

LC Class. No.: T57.95

Dewey Class. No.: 658.403

Evolutionary multi-criterion optimization = 13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4-7, 2025 : proceedings.. Part II /
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