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Evolutionary Computation in Combinatorial Optimization

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

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 A Computational Study of Neighborhood Operators for Job-Shop Scheduling Problems with Regular Objectives
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    Chapter 2 A Genetic Algorithm for Multi-component Optimization Problems: The Case of the Travelling Thief Problem
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    Chapter 3 A Hybrid Feature Selection Algorithm Based on Large Neighborhood Search
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    Chapter 4 A Memetic Algorithm to Maximise the Employee Substitutability in Personnel Shift Scheduling
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    Chapter 5 Construct, Merge, Solve and Adapt Versus Large Neighborhood Search for Solving the Multi-dimensional Knapsack Problem: Which One Works Better When?
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    Chapter 6 Decomposing SAT Instances with Pseudo Backbones
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    Chapter 7 Efficient Consideration of Soft Time Windows in a Large Neighborhood Search for the Districting and Routing Problem for Security Control
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    Chapter 8 Estimation of Distribution Algorithms for the Firefighter Problem
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    Chapter 9 LCS-Based Selective Route Exchange Crossover for the Pickup and Delivery Problem with Time Windows
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    Chapter 10 Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO
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    Chapter 11 Optimizing Charging Station Locations for Electric Car-Sharing Systems
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    Chapter 12 Selection of Auxiliary Objectives Using Landscape Features and Offline Learned Classifier
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    Chapter 13 Sparse, Continuous Policy Representations for Uniform Online Bin Packing via Regression of Interpolants
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    Chapter 14 The Weighted Independent Domination Problem: ILP Model and Algorithmic Approaches
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    Chapter 15 Towards Landscape-Aware Automatic Algorithm Configuration: Preliminary Experiments on Neutral and Rugged Landscapes
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    Chapter 16 Understanding Phase Transitions with Local Optima Networks: Number Partitioning as a Case Study
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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

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17 X users
facebook
1 Facebook page

Citations

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2 Dimensions

Readers on

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2 Mendeley
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Title
Evolutionary Computation in Combinatorial Optimization
Published by
Lecture notes in computer science, January 2017
DOI 10.1007/978-3-319-55453-2
ISBNs
978-3-31-955453-2, 978-3-31-955452-5
Editors

Bin Hu, Manuel López-Ibáñez

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

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 > Master 1 50%
Unknown 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Unknown 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 27 February 2019.
All research outputs
#2,582,347
of 23,881,329 outputs
Outputs from Lecture notes in computer science
#524
of 8,162 outputs
Outputs of similar age
#53,367
of 425,296 outputs
Outputs of similar age from Lecture notes in computer science
#20
of 149 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,162 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done particularly well, scoring higher than 93% 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 425,296 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 87% of its contemporaries.
We're also able to compare this research output to 149 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.