Titlebook: Evolutionary Multi-Criterion Optimization; 12th International C Michael Emmerich,André Deutz,Iryna Yevseyeva Conference proceedings 2023 Th

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End-to-End Pareto Set Prediction with?Graph Neural Networks for?Multi-objective Facility Locationd logistics. Many mathematical and heuristic algorithms have been developed for optimizing the FLP. In addition to the transportation cost, there are usually multiple conflicting objectives in realistic applications. It is therefore desirable to design algorithms that approximate a set of Pareto sol

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Learning to?Predict Pareto-Optimal Solutions from?Pseudo-weightstions. However, due to stochasticity involved in EMO algorithms, the uniformity in distribution of solutions cannot be guaranteed. Moreover, the follow-up decision-making activities may demand finding more solutions in specific regions on the Pareto-optimal front which may not be well-represented by

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A Relation Surrogate Model for?Expensive Multiobjective Continuous and?Combinatorial Optimizationny real-world applications. Since in surrogate model assisted evolution algorithms (SAEAs), the surrogate models from the community of machine learning are usually designed from continuous problems, and they are not suitable from combinatorial problems. For this reason, we propose a convolution rela

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An Improved Fuzzy Classifier-Based Evolutionary Algorithm for?Expensive Multiobjective Optimization Ps). However, these algorithms are usually examined on test suites with unrealistically simple Pareto sets (e.g., ZDT and DTLZ test suites). Real-world MOPs usually have complicated Pareto sets, such as a vehicle dynamic design problem and a power plant design optimization problem. Such MOPs are cha

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978-3-031-27249-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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