Evolutionary algorithms. volume 9 / Alain Pétrowski, Sana Ben-Hamida.

Format:

Book

Author/Creator:

Pétrowski, Alain, author.

Ben-Hamida, Sana, author.

Series:

Computer engineering series (London, England). Metaheuristics set.

Metaheuristics Set.

THEi Wiley ebooks.

Language:

English

Subjects (All):

Genetic algorithms.

Physical Description:

1 online resource (261 pages).

Edition:

1st ed.

Place of Publication:

London, [England] ; Hoboken, [New Jersey] : ISTE : Wiley, 2017.

System Details:

Access using campus network via VPN at home (THEi Users Only).

Summary:

Evolutionary algorithms are bio-inspired algorithms based on Darwin's theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning.

Contents:

Cover

Title Page

Copyright

Contents

Preface

1. Evolutionary Algorithms

1.1. From natural evolution to engineering

1.2. A generic evolutionary algorithm

1.3. Selection operators

1.3.1. Selection pressure

1.3.2. Genetic drift

1.3.3. Proportional selection

1.3.4. Tournament selection

1.3.5. Truncation selection

1.3.6. Environmental selection

1.3.7. Selection operators: conclusion

1.4. Variation operators and representation

1.4.1. Generalities about the variation operators

1.4.2. Crossover

1.4.3. Mutation

1.5. Binary representation

1.5.1. Crossover

1.5.2. Mutation

1.6. The simple genetic algorithm

1.7. Conclusion

2. Continuous Optimization

2.1. Introduction

2.2. Real representation and variation operators for evolutionary algorithms

2.2.1. Crossover

2.2.2. Mutation

2.3. Covariance Matrix Adaptation Evolution Strategy

2.3.1. Method presentation

2.3.2. The CMA-ES algorithm

2.4. A restart CMA Evolution Strategy

2.5. Differential Evolution (DE)

2.5.1. Initializing the population

2.5.2. The mutation operator

2.5.3. The crossover operator

2.5.4. The selection operator

2.6. Success-History based Adaptive Differential Evolution (SHADE)

2.6.1. The algorithm

2.6.2. Current-to-pbest/1 mutation

2.6.3. The success history

2.7. Particle Swarm Optimization

2.7.1. Standard Particle Swarm Algorithm 2007

2.7.2. The parameters

2.7.3. Neighborhoods

2.7.4. Swarm initialization

2.8. Experiments and performance comparisons

2.8.1. Experiments

2.8.2. Results

2.8.3. Discussion

2.9. Conclusion

2.10. Appendix: set of basic objective functions used for the experiments

3. Constrained Continuous Evolutionary Optimization

3.1. Introduction

3.1.1. The problem with Constrained Evolutionary Optimization.

3.1.2. Taxonomy

3.2. Penalization

3.2.1. Static penalties

3.2.2. Dynamic penalties

3.2.3. Adaptive penalties

3.2.4. Self-adaptive penalties

3.2.5. Stochastic ranking

3.3. Superiority of feasible solutions

3.3.1. Special penalization

3.3.2. Feasibility rules

3.4. Evolving on the feasible region

3.4.1. Searching for feasible solutions

3.4.2. Maintaining feasibility using special operators

3.5. Multi-objective methods

3.5.1. Bi-objective techniques

3.5.2. Multi-objective techniques

3.6. Parallel population approaches

3.7. Hybrid methods

3.8. Conclusion

4. Combinatorial Optimization

4.1. Introduction

4.1.1. Solution encoding

4.1.2. The knapsack problem (KP)

4.1.3. The Traveling Salesman Problem (TSP)

4.2. The binary representation and variation operators

4.2.1. Binary representation for the 0/1-KP

4.2.2. Binary representation for the TSP

4.3. Order-based Representation and variation operators

4.3.1. Crossover operators

4.3.2. Mutation operators

4.3.3. Specific operators

4.3.4. Discussion

4.4. Conclusion

5. Multi-objective Optimization

5.1. Introduction

5.2. Problem formalization

5.2.1. Pareto dominance

5.2.2. Pareto optimum

5.2.3. Multi-objective optimization algorithms

5.3. The quality indicators

5.3.1. The measure of the hypervolume or "S-metric"

5.4. Multi-objective evolutionary algorithms

5.5. Methods using a "Pareto ranking"

5.5.1. NSGA-II

5.6. Many-objective problems

5.6.1. The relaxed dominance approaches

5.6.2. Aggregation-based approaches

5.6.3. Indicator-based approaches

5.6.4. Diversity-based approaches

5.6.5. Reference set approaches

5.6.6. Preference-based approaches

5.7. Conclusion

6. Genetic Programming for Machine Learning

6.1. Introduction

6.2. Syntax tree representation.

6.2.1. Closure and sufficiency

6.2.2. Bloat control

6.3. Evolving the syntax trees

6.3.1. Initializing the population

6.3.2. Crossover

6.3.3. Mutations

6.3.4. Advanced tree-based GP

6.4. GP in action: an introductory example

6.4.1. The symbolic regression

6.4.2. First example

6.5. Alternative Genetic Programming Representations

6.5.1. Linear-based GP Representation

6.5.2. Graph-based Genetic Programming Representation

6.6. Example of application: intrusion detection in a computer system

6.6.1. Learning data

6.6.2. GP design

6.6.3. Results

6.7. Conclusion

Bibliography

Index

Other titles from iSTE in Computer Engineering

EULA.

Notes:

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed May 2, 2017).

ISBN:

9781119136385

1119136385

9781119136415

1119136415

9781119136378

1119136377

OCLC:

983734168