Applied Evolutionary Algorithms for Engineers with Python is written for students, scientists and engineers who need to apply evolutionary algorithms to practical optimization problems. The presentation of the theoretical background is complemented with didactical Python implementations of evolutionary algorithms that researchers have recently applied to complex optimization problems. Cases of successful application of evolutionary algorithms to real-world like optimization problems are presented, together with source code that allows the reader to gain insight into the idiosyncrasies of the practical application of evolutionary algorithms.

Key Features

• Includes detailed descriptions of evolutionary algorithm paradigms

• Provides didactic implementations of the algorithms in Python, a programming language that has been widely adopted by the AI community

• Discusses the application of evolutionary algorithms to real-world optimization problems

• Presents successful cases of the application of evolutionary algorithms to complex optimization problems, with auxiliary source code.

Table of Contents

SECTION I: INTRODUCTION

2. Introduction to Optimization

3. Introduction to Evolutionary Algorithms

SECTION II: SINGLE-OBJECTIVE EVOLUTIONARY ALGORITHMS

4. Swarm Optimization

5. Evolution Strategies

6. Genetic Algorithms

7. Differential Evolution

SECTION III: MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS

8. Non-Dominated Sorted Genetic Algorithm II

9. Multiobjective Evolutionary Algorithm Based on Decomposition

10. Solving Optimization Problems with Evolutionary Algorithms

11. Assessing the Performance of Evolutionary Algorithms

12. Case Study Optimal Design of a Gear Train System

13. Case Study Teaching a Legged Robot How to Walk

About the Author

Leonardo Azevedo Scardua received the D.Sc. degree in electrical engineering from the University of São Paulo, Brazil, in 2015. He has extensive engineering experience with mission-critical applications in the railway industry, having applied artificial intelligence and optimization algorithms in the development of software systems that control train traffic in many railways. He is now with the Control Engineering Department at the Federal Institute of Technology of Espírito Santo, Brazil.