Emmerich / Deutz / Wang Evolutionary Multi-Criterion Optimization

12th International Conference, EMO 2023, Leiden, The Netherlands, March 20–24, 2023, Proceedings
1. Auflage 2023
ISBN: 978-3-031-27250-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 13970, 636 Seiten, eBook

Reihe: Lecture Notes in Computer Science

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..

Zielgruppe

Research

Autoren/Hrsg.

Emmerich, Michael

Deutz, André

Wang, Hao

Kononova, Anna V.

Naujoks, Boris

Li, Ke

Miettinen, Kaisa

Yevseyeva, Iryna

Inhaltsverzeichnis

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 Multi-objective Optimization via Genetic Programming based Symbolic Regression.- Learning to Predict Pareto-optimal Solutions From Pseudo-weights.- A Relation Surrogate Model for Expensive Multiobjective Continuous and Combinatorial Optimization.- Pareto Front Upconvert by Iterative Estimation Modeling and Solution Sampling.- Pareto Front Upconvert by Iterative Estimation Modeling and Solution Sampling.- Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables.- Feature-based Benchmarking of Distance-based Multi/Many-objective Optimisation Problems: A Machine Learning Perspective.-  Benchmarking and Performance Assessment.-  Partially Degenerate Multi-Objective Test Problems.- Peak-A-Boo! GeneratingMulti-Objective Multiple Peaks Benchmark Problems with Precise Pareto Sets.- MACO: A Real-world inspired Benchmark for Multi-objective Evolutionary Algorithms.- A scalable test suite for bi-objective multidisciplinary optimisation.- Performance Evaluation of Multi-Objective Evolutionary Algorithms using Artificial and Real-World Problems.- A Novel Performance Indicator based on the Linear Assignment Problem.- A Test Suite for Multi-objective Multi-fidelity Optimization.-  Indicator Design and Complexity Analysis.-  Diversity enhancement via magnitude.- Two-Stage Greedy Approximated Hypervolume Subset Selection for Large-Scale Problems.- Two-Stage Greedy Approximated Hypervolume Subset Selection for Large-Scale Problems.- On the Computational Complexity of Efficient Non-Dominated Sort using Binary Search.-  Applications in Real World Domains.-  Evolutionary Algorithms with Machine Learning Models for Multiobjective Optimization in Epidemics Control.- Joint Price Optimization across a Portfolio of Fashion E-commerce Products.- Improving MOEA/D with Knowledge Discovery. Application to a Bi-Objective Routing Problem.- The Prism-Net Search Space Representation for Multi-Objective Building Spatial Design.- Selection Strategies for a Balanced Multi- or Many-Objective Molecular Optimization and Genetic Diversity: a Comparative Study.- A Multi-objective Evolutionary Framework for Identifying Dengue Stage-Specific Differentially Co-expressed and Functionally Enriched Gene Modules.- A Multi-objective Evolutionary Framework for Identifying Dengue Stage-Specific Differentially Co-expressed and Functionally Enriched Gene Modules. -Multiobjective Optimization of Evolutionary Neural Networks for Animal Trade Movements Prediction.- Transfer of Multi-Objectively Tuned CMA-ES Parameters to a Vehicle Dynamics Problem.-  Multi-Criteria Decision Making and Interactive Algorithms.-  Preference-Based Nonlinear Normalization for Multiobjective Optimization.- Incorporating preference information interactively in NSGA-III by the adaptation of reference vectors.- A Systematic Way of Structuring Real-World Multiobjective Optimization Problems.- IK-EMOViz: An Interactive Knowledge-based Evolutionary Multi-objective Optimization Framework.- An Interactive Decision Tree-Based Evolutionary Multi-Objective Algorithm.