Evolutionary Multi-Criterion Optimization -

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Résumé :
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...

Biographie:
Applications. Part II : Algorithm analysis...

Sommaire:
.- 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....