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.

part I|68 pages


chapter Chapter 2|11 pages

Introduction to Optimization

chapter Chapter 3|51 pages

Introduction to Evolutionary Algorithms

part II|66 pages

Single-Objective Evolutionary Algorithms

chapter Chapter 4|16 pages

Swarm Optimization

chapter Chapter 5|31 pages

Evolution Strategies

chapter Chapter 6|10 pages

Genetic Algorithms

chapter Chapter 7|8 pages

Differential Evolution

part III|24 pages

Multi-Objective Evolutionary Algorithms

chapter Chapter 8|12 pages

Non-Dominated Sorted Genetic Algorithm II

part IV|74 pages

Applying Evolutionary Algorithms

chapter Chapter 11|21 pages

Assessing the Performance of Evolutionary Algorithms

chapter Chapter 12|10 pages

Case Study: Optimal Design of a Gear Train System

chapter Chapter 13|16 pages

Case Study: Teaching a Legged Robot How to Walk