Del 15513 - Lecture Notes in Computer Science

Evolutionary Multi-Criterion Optimization

13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4–7, 2025, Proceedings, Part II

Häftad, Engelska, 2025

Av Hemant Singh, Tapabrata Ray, Joshua Knowles, Xiaodong Li, Juergen Branke, Bing Wang, Akira Oyama

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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.

Produktinformation

Utgivningsdatum2025-02-28
Mått155 x 235 x 16 mm
Vikt435 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor266
FörlagSpringer Nature Switzerland AG
ISBN9789819635375

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Artificiell intelligens inom Data och it

.- 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.