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Evolutionary Multi-Criterion Optimization

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

Table of Contents

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    Book Overview
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    Chapter 1 A Multimodal Approach for Evolutionary Multi-objective Optimization (MEMO): Proof-of-Principle Results
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    Chapter 2 Unwanted Feature Interactions Between the Problem and Search Operators in Evolutionary Multi-objective Optimization
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    Chapter 3 Neutral but a Winner! How Neutrality Helps Multiobjective Local Search Algorithms
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    Chapter 4 To DE or Not to DE? Multi-objective Differential Evolution Revisited from a Component-Wise Perspective
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    Chapter 5 Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark
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    Chapter 6 Temporal Innovization: Evolution of Design Principles Using Multi-objective Optimization
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    Chapter 7 MOEA/D-HH: A Hyper-Heuristic for Multi-objective Problems
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    Chapter 8 Using Hyper-Heuristic to Select Leader and Archiving Methods for Many-Objective Problems
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    Chapter 9 Evolutionary Multi-Criterion Optimization
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    Chapter 10 MOEA/PC: Multiobjective Evolutionary Algorithm Based on Polar Coordinates
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    Chapter 11 GD-MOEA: A New Multi-Objective Evolutionary Algorithm Based on the Generational Distance Indicator
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    Chapter 12 Experiments on Local Search for Bi-objective Unconstrained Binary Quadratic Programming
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    Chapter 13 A Bug in the Multiobjective Optimizer IBEA: Salutary Lessons for Code Release and a Performance Re-Assessment
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    Chapter 14 A Knee-Based EMO Algorithm with an Efficient Method to Update Mobile Reference Points
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    Chapter 15 A Hybrid Algorithm for Stochastic Multiobjective Programming Problem
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    Chapter 16 Parameter Tuning of MOEAs Using a Bilevel Optimization Approach
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    Chapter 17 Pareto Adaptive Scalarising Functions for Decomposition Based Algorithms
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    Chapter 18 A Bi-level Multiobjective PSO Algorithm
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    Chapter 19 An Interactive Simple Indicator-Based Evolutionary Algorithm (I-SIBEA) for Multiobjective Optimization Problems
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    Chapter 20 Combining Non-dominance, Objective-order and Spread Metric to Extend Firefly Algorithm to Multi-objective Optimization
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    Chapter 21 GACO: A Parallel Evolutionary Approach to Multi-objective Scheduling
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    Chapter 22 Kriging Surrogate Model Enhanced by Coordinate Transformation of Design Space Based on Eigenvalue Decomposition
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    Chapter 23 A Parallel Multi-Start NSGA II Algorithm for Multiobjective Energy Reduction Vehicle Routing Problem
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    Chapter 24 Evolutionary Inference of Attribute-Based Access Control Policies
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    Chapter 25 Hybrid Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization
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    Chapter 26 A Comparison of Decoding Strategies for the 0/1 Multi-objective Unit Commitment Problem
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    Chapter 27 Comparing Decomposition-Based and Automatically Component-Wise Designed Multi-Objective Evolutionary Algorithms
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    Chapter 28 Upper Confidence Bound (UCB) Algorithms for Adaptive Operator Selection in MOEA/D
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    Chapter 29 Towards Understanding Bilevel Multi-objective Optimization with Deterministic Lower Level Decisions
Overall attention for this book and its chapters
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Mentioned by

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Citations

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Readers on

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Title
Evolutionary Multi-Criterion Optimization
Published by
Lecture notes in computer science, January 2015
DOI 10.1007/978-3-319-15934-8
ISBNs
978-3-31-915933-1, 978-3-31-915934-8
Authors

Gaspar-Cunha, A., Antunes, Carlos Henggeler, Coello, Carlos Coello

Editors

Gaspar-Cunha, António, Henggeler Antunes, Carlos, Coello, Carlos Coello

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Switzerland 1 2%
Unknown 58 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 30%
Student > Master 12 20%
Student > Postgraduate 5 8%
Student > Bachelor 5 8%
Student > Doctoral Student 4 7%
Other 10 17%
Unknown 6 10%
Readers by discipline Count As %
Computer Science 30 50%
Engineering 16 27%
Design 2 3%
Economics, Econometrics and Finance 1 2%
Mathematics 1 2%
Other 4 7%
Unknown 6 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 21 March 2018.
All research outputs
#11,628,503
of 13,090,166 outputs
Outputs from Lecture notes in computer science
#6,326
of 7,352 outputs
Outputs of similar age
#236,387
of 271,072 outputs
Outputs of similar age from Lecture notes in computer science
#17
of 21 outputs
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So far Altmetric has tracked 7,352 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 271,072 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.