Description
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.
Table of Contents
.- 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.
