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Evolutionary Learning: Advances in Theories and Algorithms

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Cover of 'Evolutionary Learning: Advances in Theories and Algorithms'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Preliminaries
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    Chapter 3 Running Time Analysis: Convergence-based Analysis
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    Chapter 4 Running Time Analysis: Switch Analysis
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    Chapter 5 Running Time Analysis: Comparison and Unification
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    Chapter 6 Approximation Analysis: SEIP
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    Chapter 7 Boundary Problems of EAs
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    Chapter 8 Recombination
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    Chapter 9 Representation
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    Chapter 10 Inaccurate Fitness Evaluation
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    Chapter 11 Population
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    Chapter 12 Constrained Optimization
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    Chapter 13 Selective Ensemble
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    Chapter 14 Subset Selection
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    Chapter 15 Subset Selection: k -Submodular Maximization
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    Chapter 16 Subset Selection: Ratio Minimization
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    Chapter 17 Subset Selection: Noise
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    Chapter 18 Subset Selection: Acceleration
Attention for Chapter 6: Approximation Analysis: SEIP
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