Evolutionary Multi-Criterion Optimization: 13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4-7, 2025, Proceedings, Part II

by Hemant Singh (Editor), Tapabrata Ray (Editor), Joshua Knowles (Editor), Xiaodong Li (Editor), Juergen Branke (Editor)

Overview

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.

Product Details

ISBN-13:	9789819635375
Publisher:	Springer Nature Singapore
Publication date:	02/28/2025
Series:	Lecture Notes in Computer Science , #15513
Pages:	266
Product dimensions:	6.10(w) x 9.25(h) x (d)

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.