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Evolutionary Multi-Criterion Optimization

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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
Attention for Chapter 25: Hybrid Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization
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Chapter title
Hybrid Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization
Chapter number 25
Book title
Evolutionary Multi-Criterion Optimization
Published by
Springer, Cham, March 2015
DOI 10.1007/978-3-319-15934-8_25
Book ISBNs
978-3-31-915933-1, 978-3-31-915934-8
Authors

Florian Siegmund, Amos H. C. Ng, Kalyanmoy Deb

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 29%
Professor 1 14%
Researcher 1 14%
Student > Master 1 14%
Unknown 2 29%
Readers by discipline Count As %
Computer Science 4 57%
Business, Management and Accounting 1 14%
Engineering 1 14%
Unknown 1 14%