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Learning and Intelligent Optimization

Overview of attention for book
Cover of 'Learning and Intelligent Optimization'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 A Column Generation Heuristic for the General Vehicle Routing Problem
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    Chapter 2 A Combination of Evolutionary Algorithm, Mathematical Programming, and a New Local Search Procedure for the Just-In-Time Job-Shop Scheduling Problem
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    Chapter 3 A Math-Heuristic Algorithm for the DNA Sequencing Problem
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    Chapter 4 A Randomized Iterated Greedy Algorithm for the Founder Sequence Reconstruction Problem
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    Chapter 5 Adaptive “Anytime” Two-Phase Local Search
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    Chapter 6 Adaptive Filter SQP
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    Chapter 7 Algorithm Selection as a Bandit Problem with Unbounded Losses
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    Chapter 8 Bandit-Based Estimation of Distribution Algorithms for Noisy Optimization: Rigorous Runtime Analysis
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    Chapter 9 Consistency Modifications for Automatically Tuned Monte-Carlo Tree Search
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    Chapter 10 Distance Functions, Clustering Algorithms and Microarray Data Analysis
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    Chapter 11 Gaussian Process Assisted Particle Swarm Optimization
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    Chapter 12 Learning of Highly-Filtered Data Manifold Using Spectral Methods
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    Chapter 13 Multiclass Visual Classifier Based on Bipartite Graph Representation of Decision Tables
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    Chapter 14 A Linear Approximation of the Value Function of an Approximate Dynamic Programming Approach for the Ship Scheduling Problem
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    Chapter 15 A Multilevel Scheme with Adaptive Memory Strategy for Multiway Graph Partitioning
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    Chapter 16 A Network Approach for Restructuring the Korean Freight Railway Considering Customer Behavior
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    Chapter 17 A Parallel Multi-Objective Evolutionary Algorithm for Phylogenetic Inference
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    Chapter 18 Learning and Intelligent Optimization
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    Chapter 19 Generative Topographic Mapping for Dimension Reduction in Engineering Design
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    Chapter 20 Learning Decision Trees for the Analysis of Optimization Heuristics
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    Chapter 21 On the Coordination of Multidisciplinary Design Optimization Using Expert Systems
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    Chapter 22 On the Potentials of Parallelizing Large Neighbourhood Search for Rich Vehicle Routing Problems
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    Chapter 23 Optimized Ensembles for Clustering Noisy Data
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    Chapter 24 Stochastic Local Search for the Optimization of Secondary Structure Packing in Proteins
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    Chapter 25 Systematic Improvement of Monte-Carlo Tree Search with Self-generated Neural-Networks Controllers
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    Chapter 26 Grapheur: A Software Architecture for Reactive and Interactive Optimization
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    Chapter 27 The EvA2 Optimization Framework
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    Chapter 28 Feature Extraction from Optimization Data via DataModeler’s Ensemble Symbolic Regression
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    Chapter 29 Understanding TSP Difficulty by Learning from Evolved Instances
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    Chapter 30 Time-Bounded Sequential Parameter Optimization
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    Chapter 31 Pitfalls in Instance Generation for Udine Timetabling
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    Chapter 32 A Study of the Parallelization of the Multi-Objective Metaheuristic MOEA/D
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    Chapter 33 An Interactive Evolutionary Multi-objective Optimization Method Based on Polyhedral Cones
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    Chapter 34 On the Distribution of EMOA Hypervolumes
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    Chapter 35 Adapting to a Realistic Decision Maker: Experiments towards a Reactive Multi-objective Optimizer
Overall attention for this book and its chapters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (75th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

twitter
2 X users
wikipedia
2 Wikipedia pages
video
1 YouTube creator

Citations

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5 Dimensions

Readers on

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6 Mendeley
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Title
Learning and Intelligent Optimization
Published by
Lecture notes in computer science, January 2010
DOI 10.1007/978-3-642-13800-3
ISBNs
978-3-64-213799-0, 978-3-64-213800-3
Authors

Christian Blum, Roberto Battiti

Editors

Blum, Christian, Battiti, Roberto

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 33%
Professor 1 17%
Researcher 1 17%
Professor > Associate Professor 1 17%
Unknown 1 17%
Readers by discipline Count As %
Computer Science 3 50%
Engineering 2 33%
Unknown 1 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 17 January 2019.
All research outputs
#6,042,299
of 22,805,349 outputs
Outputs from Lecture notes in computer science
#1,991
of 8,126 outputs
Outputs of similar age
#39,654
of 163,943 outputs
Outputs of similar age from Lecture notes in computer science
#31
of 189 outputs
Altmetric has tracked 22,805,349 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 8,126 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 75% 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 163,943 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 75% of its contemporaries.
We're also able to compare this research output to 189 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.