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Evolutionary Multi-Criterion Optimization

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Cover of 'Evolutionary Multi-Criterion Optimization'

Table of Contents

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    Book Overview
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    Chapter 1 On the Effect of Scalarising Norm Choice in a ParEGO implementation
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    Chapter 2 Multi-objective Big Data Optimization with jMetal and Spark
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    Chapter 3 An Empirical Assessment of the Properties of Inverted Generational Distance on Multi- and Many-Objective Optimization
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    Chapter 4 Solving the Bi-objective Traveling Thief Problem with Multi-objective Evolutionary Algorithms
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    Chapter 5 Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation
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    Chapter 6 The Multiobjective Shortest Path Problem Is NP-Hard, or Is It?
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    Chapter 7 Angle-Based Preference Models in Multi-objective Optimization
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    Chapter 8 Quantitative Performance Assessment of Multiobjective Optimizers: The Average Runtime Attainment Function
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    Chapter 9 A Multiobjective Strategy to Allocate Roadside Units in a Vehicular Network with Guaranteed Levels of Service
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    Chapter 10 An Approach for the Local Exploration of Discrete Many Objective Optimization Problems
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    Chapter 11 A Note on the Detection of Outliers in a Binary Outranking Relation
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    Chapter 12 Classifying Metamodeling Methods for Evolutionary Multi-objective Optimization: First Results
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    Chapter 13 Weighted Stress Function Method for Multiobjective Evolutionary Algorithm Based on Decomposition
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    Chapter 14 Timing the Decision Support for Real-World Many-Objective Optimization Problems
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    Chapter 15 On the Influence of Altering the Action Set on PROMETHEE II’s Relative Ranks
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    Chapter 16 Peek – Shape – Grab: A Methodology in Three Stages for Approximating the Non-dominated Points of Multiobjective Discrete/Combinatorial Optimization Problems with a Multiobjective Metaheuristic
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    Chapter 17 A New Reduced-Length Genetic Representation for Evolutionary Multiobjective Clustering
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    Chapter 18 A Fast Incremental BSP Tree Archive for Non-dominated Points
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    Chapter 19 Adaptive Operator Selection for Many-Objective Optimization with NSGA-III
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    Chapter 20 On Using Decision Maker Preferences with ParEGO
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    Chapter 21 First Investigations on Noisy Model-Based Multi-objective Optimization
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    Chapter 22 Fusion of Many-Objective Non-dominated Solutions Using Reference Points
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    Chapter 23 An Expedition to Multimodal Multi-objective Optimization Landscapes
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    Chapter 24 Neutral Neighbors in Bi-objective Optimization: Distribution of the Most Promising for Permutation Problems
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    Chapter 25 Multi-objective Adaptation of a Parameterized GVGAI Agent Towards Several Games
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    Chapter 26 Towards Standardized and Seamless Integration of Expert Knowledge into Multi-objective Evolutionary Optimization Algorithms
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    Chapter 27 Empirical Investigations of Reference Point Based Methods When Facing a Massively Large Number of Objectives: First Results
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    Chapter 28 Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms
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    Chapter 29 A Fitness Landscape Analysis of Pareto Local Search on Bi-objective Permutation Flowshop Scheduling Problems
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    Chapter 30 Dimensionality Reduction Approach for Many-Objective Vehicle Routing Problem with Demand Responsive Transport
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    Chapter 31 Heterogeneous Evolutionary Swarms with Partial Redundancy Solving Multi-objective Tasks
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    Chapter 32 Multiple Metamodels for Robustness Estimation in Multi-objective Robust Optimization
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    Chapter 33 Predator-Prey Techniques for Solving Multiobjective Scheduling Problems for Unrelated Parallel Machines
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    Chapter 34 An Overview of Weighted and Unconstrained Scalarizing Functions
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    Chapter 35 Multi-objective Representation Setups for Deformation-Based Design Optimization
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    Chapter 36 Design Perspectives of an Evolutionary Process for Multi-objective Molecular Optimization
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    Chapter 37 Towards a Better Balance of Diversity and Convergence in NSGA-III: First Results
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    Chapter 38 A Comparative Study of Fast Adaptive Preference-Guided Evolutionary Multi-objective Optimization
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    Chapter 39 A Population-Based Algorithm for Learning a Majority Rule Sorting Model with Coalitional Veto
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    Chapter 40 Injection of Extreme Points in Evolutionary Multiobjective Optimization Algorithms
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    Chapter 41 The Impact of Population Size, Number of Children, and Number of Reference Points on the Performance of NSGA-III
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    Chapter 42 Multi-objective Optimization for Liner Shipping Fleet Repositioning
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    Chapter 43 Surrogate-Assisted Partial Order-Based Evolutionary Optimisation
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    Chapter 44 Hypervolume Indicator Gradient Ascent Multi-objective Optimization
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    Chapter 45 Toward Step-Size Adaptation in Evolutionary Multiobjective Optimization
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    Chapter 46 Computing 3-D Expected Hypervolume Improvement and Related Integrals in Asymptotically Optimal Time
Attention for Chapter 28: Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms
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Chapter title
Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms
Chapter number 28
Book title
Evolutionary Multi-Criterion Optimization
Published in
Lecture notes in computer science, February 2017
DOI 10.1007/978-3-319-54157-0_28
Book ISBNs
978-3-31-954156-3, 978-3-31-954157-0
Authors

Longmei Li, Iryna Yevseyeva, Vitor Basto-Fernandes, Heike Trautmann, Ning Jing, Michael Emmerich

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 35%
Lecturer 3 15%
Researcher 2 10%
Student > Bachelor 1 5%
Lecturer > Senior Lecturer 1 5%
Other 1 5%
Unknown 5 25%
Readers by discipline Count As %
Computer Science 12 60%
Earth and Planetary Sciences 1 5%
Decision Sciences 1 5%
Engineering 1 5%
Unknown 5 25%

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