Evolutionary Multi-Criterion Optimization

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

AvHemant Singh,Tapabrata Ray

Häftad, Engelska, 2025

Del 15513 i serien Lecture Notes in Computer Science

642 kr

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Beskrivning

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

Utgivningsdatum:2025-02-28
Mått:155 x 235 x 16 mm
Vikt:435 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:266
Förlag:Springer Nature Switzerland AG
ISBN:9789819635375

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Artificiell intelligens inom Data och IT

Innehållsförteckning

.- 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.