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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 2: Multi-objective Big Data Optimization with jMetal and Spark
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (71st percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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