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Evolutionary Computation in Combinatorial Optimization

Overview of attention for book
Cover of 'Evolutionary Computation in Combinatorial Optimization'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Optimizing Prices and Periods in Time-of-use Electricity Tariff Design Using Bilevel Programming
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    Chapter 2 An Algebraic Approach for the Search Space of Permutations with Repetition
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    Chapter 3 A Comparison of Genetic Representations for Multi-objective Shortest Path Problems on Multigraphs
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    Chapter 4 The Univariate Marginal Distribution Algorithm Copes Well with Deception and Epistasis
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    Chapter 5 A Beam Search Approach to the Traveling Tournament Problem
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    Chapter 6 Cooperative Parallel SAT Local Search with Path Relinking
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    Chapter 7 Dynamic Compartmental Models for Large Multi-objective Landscapes and Performance Estimation
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    Chapter 8 Fitness Landscape Analysis of Automated Machine Learning Search Spaces
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    Chapter 9 On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D
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    Chapter 10 A Grouping Genetic Algorithm for Multi Depot Pickup and Delivery Problems with Time Windows and Heterogeneous Vehicle Fleets
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    Chapter 11 MILPIBEA: Algorithm for Multi-objective Features Selection in (Evolving) Software Product Lines
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    Chapter 12 A Group Genetic Algorithm for Resource Allocation in Container-Based Clouds
  14. Altmetric Badge
    Chapter 13 The Local Optima Level in Chemotherapy Schedule Optimisation
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    Chapter 14 Genetic Programming with Adaptive Search Based on the Frequency of Features for Dynamic Flexible Job Shop Scheduling
Attention for Chapter 9: On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
4 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
2 Mendeley
Summary Twitter Dimensions citations
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Chapter title
On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D
Chapter number 9
Book title
Evolutionary Computation in Combinatorial Optimization
Published in
arXiv, April 2020
DOI 10.1007/978-3-030-43680-3_9
Book ISBNs
978-3-03-043679-7, 978-3-03-043680-3
Authors

Geoffrey Pruvost, Bilel Derbel, Arnaud Liefooghe, Ke Li, Qingfu Zhang, Pruvost, Geoffrey, Derbel, Bilel, Liefooghe, Arnaud, Li, Ke, Zhang, Qingfu

View on publisher site

Twitter Demographics

The data shown below were collected from the profiles of 4 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 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 50%
Unknown 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Unknown 1 50%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 16 April 2020.
All research outputs
#15,075,400
of 23,201,298 outputs
Outputs from arXiv
#327,846
of 955,194 outputs
Outputs of similar age
#221,200
of 375,269 outputs
Outputs of similar age from arXiv
#11,627
of 30,999 outputs
Altmetric has tracked 23,201,298 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 955,194 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 60% 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 375,269 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 30,999 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.

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