Del 13970 - Lecture Notes in Computer Science

Evolutionary Multi-Criterion Optimization

12th International Conference, EMO 2023, Leiden, The Netherlands, March 20–24, 2023, Proceedings

Häftad, Engelska, 2023

Av Michael Emmerich, André Deutz, Hao Wang, Anna V. Kononova, Boris Naujoks, Ke Li, Kaisa Miettinen, Iryna Yevseyeva, Andre Deutz, Hao Deutz, André

1 189 kr

Beställningsvara. Skickas inom 7-10 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

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..

Produktinformation

Utgivningsdatum2023-02-21
Mått155 x 235 x 36 mm
Vikt978 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor636
FörlagSpringer International Publishing AG
ISBN9783031272493

Tillhör följande kategorier

Tillämpad datateknik inom Data och it
Informationsteknik: allmänt inom Data och it
Systemvetenskap och AI inom Data och it
Hårdvara inom Data och it
Artificiell intelligens inom Data och it

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.