【2019新书】Evolutionary Algorithms and Neural Networks（进化算法和神经网络）.pdf

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Preface

Contents

Acronyms

Part I Evolutionary Algorithms

1 Introduction to Evolutionary Single-Objective Optimisation

1.1 Introduction

1.2 Single-Objective Optimisation

1.3 Search Landscape

1.4 Penalty Functions for Handling Constraints

1.5 Classification of Optimisation Algorithms

1.6 Exploration and Exploitation

1.7 Classification of Population-Based Optimisation Algorithms

1.8 Conclusion

References

2 Particle Swarm Optimisation

2.1 Introduction

2.2 Inspiration

2.3 Mathematical Model of PSO

2.4 Analysis of PSO

2.5 Standard PSO

2.6 Binary PSO

2.7 Conclusion

References

3 Ant Colony Optimisation

3.1 Introduction

3.2 Inspiration

3.3 Mathematical Model

3.3.1 Construction Phase

3.3.2 Pheromone Phase

3.3.3 Daemon Phase

3.3.4 Max-Min Ant System

3.3.5 Ant Colony System

3.3.6 Continuous Ant Colony

3.4 ACO for Combinatorial Optimisation Problems

3.5 Conclusion

References

4 Genetic Algorithm

4.1 Introduction

4.2 Inspiration

4.3 Initial Population

4.4 Selection

4.5 Crossover (Recombination)

4.6 Mutation

4.7 Experiments When Changing the Mutation Rate

4.8 Experiment When Changing the Crossover Rate

4.9 Conclusion

References

5 Biogeography-Based Optimisation

5.1 Introduction

5.2 Inspiration

5.3 Mathematical Model

5.4 Solving Engineering Design Problems with BBO

5.4.1 Three-Bar Truss Design Problem

5.4.2 I-Beam Design Problem

5.4.3 Welded Beam Design Problem

5.4.4 Cantilever Beam Design Problem

5.4.5 Tension/compression Spring Design

5.4.6 Pressure Vessel Design

5.4.7 Gear Train Design Problem

References

Part II Evolutionary Neural Networks

6 Evolutionary Feedforward Neural Networks

6.1 Introduction

6.2 Feedforward Neural Networks

6.3 Designing Evolutionary FNNs

6.4 Results

6.4.1 XOR Dataset

6.4.2 Balloon Dataset

6.4.3 Iris Dataset

6.4.4 Breast Cancer Dataset

6.4.5 Heart Dataset

6.4.6 Discussion and Analysis of the Results

6.5 Conclusion

References

7 Evolutionary Multi-layer Perceptron

7.1 Introduction

7.2 Multi-layer Perceptrons

7.3 Evolutionary Multi-layer Percenptron

7.4 Results

7.4.1 Sigmoid Function

7.4.2 Cosine Function

7.4.3 Sine Function

7.4.4 Sphere Function

7.4.5 Griewank Function

7.4.6 Rosenbrock Function

7.4.7 Comparison with BP

7.5 Conclusion

References

8 Evolutionary Radial Basis Function Networks

8.1 Introduction

8.2 Radial Based Function Neural Networks

8.2.1 Classical Radial Basis Function Network Training

8.3 Evolutionary Algorithms for Training Radial Basis Function Networks

8.4 Experiments and Results

8.4.1 Experimental Setup

8.4.2 Datasets Description

8.4.3 Results

8.5 Conclusion

References

9 Evolutionary Deep Neural Networks

9.1 Introduction

9.2 Datasets and Evolutionary Deep Neural Networks

9.2.1 Datasets

9.2.2 Evolutionary Neural Networks

9.3 Results and Discussion

9.3.1 NN with BP

9.3.2 Training FNN Using PSO for the Dataset

9.3.3 Number of Hidden Nodes

9.3.4 Changing Nodes in One Layer

9.3.5 Changing the Number of Layers (Deep Learning)

9.3.6 Feature Selection

9.4 Conclusion

References

Studies in Computational Intelligence 780 Seyedali Mirjalili Evolutionary Algorithms and Neural Networks Theory and Applications

Studies in Computational Intelligence Volume 780 Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: kacprzyk@ibspan.waw.pl

The series “Studies in Computational Intelligence” (SCI) publishes new develop- ments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the ﬁelds of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artiﬁcial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the worldwide distribution, which enable both wide and rapid dissemination of research output. More information about this series at http://www.springer.com/series/7092

Seyedali Mirjalili Evolutionary Algorithms and Neural Networks Theory and Applications 123

Seyedali Mirjalili Institute for Integrated and Intelligent Systems Grifﬁth University Brisbane, QLD Australia ISSN 1860-949X Studies in Computational Intelligence ISBN 978-3-319-93024-4 https://doi.org/10.1007/978-3-319-93025-1 ISSN 1860-9503 (electronic) ISBN 978-3-319-93025-1 (eBook) Library of Congress Control Number: 2018943379 © Springer International Publishing AG, part of Springer Nature 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microﬁlms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, in this publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations. trademarks, service marks, etc. Printed on acid-free paper This Springer imprint is published by the registered company Springer International Publishing AG part of Springer Nature The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To my mother and father

Preface This book focuses on both theory and application of evolutionary algorithms and artiﬁcial neural networks. An attempt is made to make a bridge between these two ﬁelds with an emphasis on real-world applications. Part I presents well-regarded and recent evolutionary algorithms and optimisa- tion techniques. Quantitative and qualitative analyses of each algorithm are per- formed to understand the behaviour and investigate their potentials to be used in conjunction with artiﬁcial neural networks. Part II reviews the literature of several types of artiﬁcial neural networks including feedforward neural networks, multi-layer perceptrons, and radial basis function network. It then proposes evolutionary version of these techniques in several chapters. Most of the challenges that have to be addressed when training artiﬁcial neural networks using evolutionary algorithms are discussed in detail. Due to the simplicity of the proposed techniques and ﬂexibility, readers from any ﬁeld of study can employ them for classiﬁcation, clustering, approximation, and prediction problems. In addition, the book demonstrates the application of the proposed algorithms in several ﬁelds, which shed lights to solve new problems. The book provides a tutorial on how to design, adapt, and evaluate artiﬁcial neural networks as well, which would be beneﬁcial for the readers interested in developing learning algorithms for artiﬁcial neural networks. Brisbane, Australia April 2018 Dr. Seyedali Mirjalili vii

Contents Part I Evolutionary Algorithms 1 Introduction to Evolutionary Single-Objective Optimisation . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Single-Objective Optimisation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Search Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Penalty Functions for Handling Constraints . . . . . . . . . . . . . . . . . 1.5 Classiﬁcation of Optimisation Algorithms . . . . . . . . . . . . . . . . . . 1.6 Exploration and Exploitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Classiﬁcation of Population-Based Optimisation Algorithms . . . . . 1.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Particle Swarm Optimisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Inspiration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Mathematical Model of PSO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Analysis of PSO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Standard PSO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Binary PSO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Ant Colony Optimisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Inspiration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Mathematical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Construction Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Pheromone Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3 4 6 8 10 11 12 13 13 15 15 15 18 19 21 24 30 31 33 33 33 34 35 36 ix

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