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Nature-Inspired Computing and Optimization

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Cover of 'Nature-Inspired Computing and Optimization'

Table of Contents

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    Book Overview
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    Chapter 1 The Nature of Nature: Why Nature-Inspired Algorithms Work
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    Chapter 2 Multimodal Function Optimization Using an Improved Bat Algorithm in Noise-Free and Noisy Environments
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    Chapter 3 Multi-objective Ant Colony Optimisation in Wireless Sensor Networks
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    Chapter 4 Generating the Training Plans Based on Existing Sports Activities Using Swarm Intelligence
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    Chapter 5 Limiting Distribution and Mixing Time for Genetic Algorithms
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    Chapter 6 Permutation Problems, Genetic Algorithms, and Dynamic Representations
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    Chapter 7 Hybridization of the Flower Pollination Algorithm—A Case Study in the Problem of Generating Healthy Nutritional Meals for Older Adults
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    Chapter 8 Nature-inspired Algorithm-based Optimization for Beamforming of Linear Antenna Array System
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    Chapter 9 Multi-Agent Optimization of Resource-Constrained Project Scheduling Problem Using Nature-Inspired Computing
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    Chapter 10 Application of Learning Classifier Systems to Gene Expression Analysis in Synthetic Biology
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    Chapter 11 Ant Colony Optimization for Semantic Searching of Distributed Dynamic Multiclass Resources
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    Chapter 12 Adaptive Virtual Topology Control Based on Attractor Selection
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    Chapter 13 CBO-Based TDR Approach for Wiring Network Diagnosis
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    Chapter 14 Morphological Filters: An Inspiration from Natural Geometrical Erosion and Dilation
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    Chapter 15 Brain Action Inspired Morphological Image Enhancement
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    Chapter 16 Path Generation for Software Testing: A Hybrid Approach Using Cuckoo Search and Bat Algorithm
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    Chapter 17 An Improved Spider Monkey Optimization for Solving a Convex Economic Dispatch Problem
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    Chapter 18 Chance-Constrained Fuzzy Goal Programming with Penalty Functions for Academic Resource Planning in University Management Using Genetic Algorithm
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    Chapter 19 Swarm Intelligence: A Review of Algorithms
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Title
Nature-Inspired Computing and Optimization
Published by
Springer International Publishing, January 2017
DOI 10.1007/978-3-319-50920-4
ISBNs
978-3-31-950919-8, 978-3-31-950920-4
Editors

Srikanta Patnaik, Xin-She Yang, Kazumi Nakamatsu

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 14%
Student > Bachelor 5 9%
Student > Ph. D. Student 5 9%
Student > Doctoral Student 2 3%
Professor > Associate Professor 2 3%
Other 3 5%
Unknown 33 57%
Readers by discipline Count As %
Engineering 10 17%
Computer Science 10 17%
Environmental Science 1 2%
Mathematics 1 2%
Social Sciences 1 2%
Other 1 2%
Unknown 34 59%