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Evolutionary Multi-Criterion Optimization: 12th International Conference, EMO 2023, Leiden, The Netherlands, March 2024, 2023, Proceedings [Paperback]

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  • Category: Books (Computers)
  • ISBN-10:  3031272498
  • ISBN-10:  3031272498
  • ISBN-13:  9783031272493
  • ISBN-13:  9783031272493
  • Publisher:  Springer
  • Publisher:  Springer
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-Apr-2023
  • Pub Date:  01-Apr-2023
  • SKU:  3031272498-11-SPRI
  • SKU:  3031272498-11-SPRI
  • Pages:  636
  • Pages:  636
  • Item ID: 105250509
  • List Price: $99.99
  • Seller: ShopSpell
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  • Delivery by: Oct 16 to Oct 18
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This book constitutes the refereed proceedings of the 12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2022 held in Leiden, The Netherlands, during March 20-24, 2023.

The 44 regular papers presented in this book were carefully reviewed and selected from 65 submissions.

The papers are divided into the following topical sections: Algorithm Design and Engineering; Machine Learning and Multi-criterion Optimization; Benchmarking and Performance Assessment; Indicator Design and Complexity Analysis; Applications in Real World Domains; and Multi-Criteria Decision Making and Interactive Algorithms..


Algorithm Design and Engineering.- Visual Exploration of the Effect of Constraint Handling in Multiobjective Optimization.- A Two-stage Algorithm for Integer Multiobjective Simulation Optimization.- RegEMO: Sacrificing Pareto-Optimality for Regularity in Multi-objective Problem-Solving.- Cooperative coevolutionary NSGA-II with Linkage Measurement Minimization for Large-scale Multi-objective Optimization.- Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts.- Eliminating Non-dominated Sorting from NSGA-III.- Scalability of Multi-Objective Evolutionary Algorithms for Solving Real-World Complex Optimization Problems.- Machine Learning and Multi-criterion Optimization.- Multi-Objective Learning using HV Maximization.- Sparse Adversarial Attack via Bi-Objective Optimization.- Investigating Innovized Progress Operators with Different Machine Learning Methods.- End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location.- Online Learning Hyper-Heuristics in Multi-Objective Evolutionary Algorithms.- Surrogate-assisted lS(
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