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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
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    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
Attention for Chapter 13: Sparse, Continuous Policy Representations for Uniform Online Bin Packing via Regression of Interpolants
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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Chapter title
Sparse, Continuous Policy Representations for Uniform Online Bin Packing via Regression of Interpolants
Chapter number 13
Book title
Evolutionary Computation in Combinatorial Optimization
Published in
Lecture notes in computer science, March 2017
DOI 10.1007/978-3-319-55453-2_13
Book ISBNs
978-3-31-955452-5, 978-3-31-955453-2
Authors

John H. Drake, Jerry Swan, Geoff Neumann, Ender Özcan

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 23%
Student > Doctoral Student 2 15%
Researcher 1 8%
Lecturer > Senior Lecturer 1 8%
Unknown 6 46%
Readers by discipline Count As %
Computer Science 4 31%
Engineering 2 15%
Unknown 7 54%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 23 February 2018.
All research outputs
#6,993,535
of 22,965,074 outputs
Outputs from Lecture notes in computer science
#2,265
of 8,137 outputs
Outputs of similar age
#112,199
of 307,884 outputs
Outputs of similar age from Lecture notes in computer science
#20
of 113 outputs
Altmetric has tracked 22,965,074 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 8,137 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 71% 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 307,884 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 113 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.