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ORCID13th EMO 2025: Canberra, ACT, Australia - Part II
- Hemant K. Singh
, Tapabrata Ray, Joshua D. Knowles, Xiaodong Li, Juergen Branke, Bing Wang, Akira Oyama:
Evolutionary Multi-Criterion Optimization - 13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4-7, 2025, Proceedings, Part II. Lecture Notes in Computer Science 15513, Springer 2025, ISBN 978-981-96-3537-5
Algorithm Analysis
- Hisao Ishibuchi, Lie Meng Pang:
Visual Explanations of Some Problematic Search Behaviors of Frequently-Used EMO Algorithms. 3-16 - Tianye Shu, Hisao Ishibuchi, Yang Nan, Lie Meng Pang:
Numerical Analysis of Pareto Set Modeling. 17-30 - Qiaozhi Zhang, Miqing Li, Ke Tang, Xin Yao:
When Is Non-deteriorating Population Update in MOEAs Beneficial? 31-45 - Sumit Mishra, Ved Prakash, Carlos A. Coello Coello:
Analysis of Merge Non-dominated Sorting Algorithm. 46-57 - Ksenia Pereverdieva, André H. Deutz, Tessa Ezendam, Thomas Bäck, Hèrm Hofmeyer, Michael T. M. Emmerich:
Comparative Analysis of Indicators for Multi-objective Diversity Optimization. 58-71 - Yang Nan, Hisao Ishibuchi, Lie Meng Pang:
Performance Analysis of Constrained Evolutionary Multi-objective Optimization Algorithms on Artificial and Real-World Problems. 72-84 - Angel E. Rodriguez-Fernandez, Carlos Ignacio Hernández Castellanos, Oliver Schütze:
On the Approximation of the Entire Pareto Front of a Constrained Multi-objective Optimization Problem. 85-98 - Yang Nan, Hisao Ishibuchi, Lie Meng Pang:
Small Population Size is Enough in Many Cases with External Archives. 99-113
Surrogates and Machine Learning
- Jack M. Buckingham, Sebastian Rojas-Gonzalez, Juergen Branke:
Knowledge Gradient for Multi-objective Bayesian Optimization with Decoupled Evaluations. 117-132 - Qingyu Mo, João A. Duro, Robin C. Purshouse:
Surrogate Strategies for Scalarisation-Based Multi-objective Bayesian Optimizers. 133-146 - Balija Santoshkumar, Kalyanmoy Deb:
A Mixed-Fidelity Evaluation Algorithm for Efficient Constrained Multi- and Many-Objective Optimization: First Results. 147-162 - Yihao Yang, Yuji Sato:
Efficient and Accurate Surrogate-Assisted Approach to Multi-objective Optimization Using Deep Neural Networks. 163-177 - Fei Liu, Xi Lin, Shunyu Yao, Zhenkun Wang, Xialiang Tong, Mingxuan Yuan, Qingfu Zhang:
Large Language Model for Multiobjective Evolutionary Optimization. 178-191 - Franz Herm, Atanu Mazumdar, Tinkle Chugh:
Multi-objective Multi-agent Reinforcement Learning for Autonomous Driving in Mixed-Traffic Environments. 192-207 - Dasun Shalila Balasooriya, Alan Blair, Ben Wilks, Craig A. Wheeler, Tahir Jauhar, Stephan K. Chalup:
Parallel TD3 for Policy Gradient-Based Multi-condition Multi-objective Optimisation. 208-222
Multi-criteria Decision Support
- Deepanshu Yadav, Palaniappan Ramu, Kalyanmoy Deb:
Reliability-Based MCDM Using Objective Preferences Under Variable Uncertainty. 225-240 - Bhupinder Singh Saini, Hemant Kumar Singh, Babooshka Shavazipour, Kaisa Miettinen:
An Efficient Iterative Approach for Uniformly Representing Pareto Fronts. 241-256
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