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Biologically-Inspired Optimisation Methods

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Cover of 'Biologically-Inspired Optimisation Methods'

Table of Contents

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    Book Overview
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    Chapter 1 Evolution’s Niche in Multi-Criterion Problem Solving
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    Chapter 2 Applications of Parallel Platforms and Models in Evolutionary Multi-Objective Optimization
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    Chapter 3 Biologically-Inspired Optimisation Methods
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    Chapter 4 Dynamic Problems and Nature Inspired Meta-heuristics
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    Chapter 5 Relaxation Labelling Using Distributed Neural Networks
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    Chapter 6 Biologically-Inspired Optimisation Methods
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    Chapter 7 Niching for Ant Colony Optimisation
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    Chapter 8 Biologically-Inspired Optimisation Methods
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    Chapter 9 The Radio Network Design Optimization Problem
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    Chapter 10 Strategies for Decentralised Balancing Power
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    Chapter 11 An Analysis of Dynamic Mutation Operators for Conformational Sampling
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    Chapter 12 Evolving Computer Chinese Chess Using Guided Learning
Attention for Chapter 4: Dynamic Problems and Nature Inspired Meta-heuristics
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