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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 20: Combining Non-dominance, Objective-order and Spread Metric to Extend Firefly Algorithm to Multi-objective Optimization
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Chapter title
Combining Non-dominance, Objective-order and Spread Metric to Extend Firefly Algorithm to Multi-objective Optimization
Chapter number 20
Book title
Evolutionary Multi-Criterion Optimization
Published by
Springer, Cham, March 2015
DOI 10.1007/978-3-319-15934-8_20
Book ISBNs
978-3-31-915933-1, 978-3-31-915934-8
Authors

M. Fernanda P. Costa, Ana Maria A. C. Rocha, Edite M. G. P. Fernandes

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 4 31%
Researcher 2 15%
Student > Bachelor 1 8%
Student > Master 1 8%
Professor > Associate Professor 1 8%
Other 0 0%
Unknown 4 31%
Readers by discipline Count As %
Engineering 4 31%
Computer Science 4 31%
Mathematics 1 8%
Unknown 4 31%