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Evolutionary Multi-Criterion Optimization

Overview of attention for book
Cover of 'Evolutionary Multi-Criterion Optimization'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    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
  47. Altmetric Badge
    Chapter 46 Computing 3-D Expected Hypervolume Improvement and Related Integrals in Asymptotically Optimal Time
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
1 news outlet
twitter
8 tweeters
q&a
1 Q&A thread

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
14 Mendeley
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Title
Evolutionary Multi-Criterion Optimization
Published by
Lecture notes in computer science, January 2017
DOI 10.1007/978-3-319-54157-0
ISBNs
978-3-31-954156-3, 978-3-31-954157-0
Editors

Heike Trautmann, Günter Rudolph, Kathrin Klamroth, Oliver Schütze, Margaret Wiecek, Yaochu Jin, Christian Grimme

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 21%
Student > Master 2 14%
Professor 2 14%
Researcher 2 14%
Student > Bachelor 1 7%
Other 1 7%
Unknown 3 21%
Readers by discipline Count As %
Computer Science 4 29%
Engineering 2 14%
Mathematics 2 14%
Biochemistry, Genetics and Molecular Biology 1 7%
Environmental Science 1 7%
Other 0 0%
Unknown 4 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 18. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 December 2019.
All research outputs
#1,197,235
of 16,522,185 outputs
Outputs from Lecture notes in computer science
#239
of 7,684 outputs
Outputs of similar age
#31,022
of 269,702 outputs
Outputs of similar age from Lecture notes in computer science
#3
of 124 outputs
Altmetric has tracked 16,522,185 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,684 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 269,702 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 124 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.