Applied Evolutionary Algorithms for Engineers Using Python

This book provides a theoretical and practical introduction to evolutionary algorithms for solving optimization problems in engineering and science. It covers Python implementations of popular algorithms, discusses real-world applications, and highlights successful cases.

Format: Hardback
Length: 246 pages
Publication date: 15 June 2021
Publisher: Taylor & Francis Ltd

This comprehensive book is designed for individuals who aspire to utilize evolutionary algorithms to address engineering and scientific challenges. Its primary objective is to provide the theoretical foundation required to comprehend the presented evolutionary algorithms and their limitations, while also delving into crucial themes that play a pivotal role in the successful application of these algorithms to real-world problems. To enhance understanding, the theoretical descriptions are accompanied by practical Python implementations of the algorithms, offering readers a chance to consolidate their knowledge and establish a solid foundation for applying evolutionary algorithms to optimization challenges in their respective fields.

Python has been selected as the programming language for these implementations due to its widespread adoption within the Artificial Intelligence community. Those with prior experience in high-level languages like MATLAB™ will find it effortless to navigate the Python implementations of the evolutionary algorithms presented in the book.

Rather than attempting to encompass a vast array of existing evolutionary algorithms, the book concentrates on those that researchers have recently applied to challenging optimization problems, such as control problems with continuous action spaces and the training of high-dimensional convolutional neural networks. The fundamental characteristics of real-world optimization problems are presented, along with guidance on their appropriate utilization in the context of evolutionary algorithms.

To reinforce the applied nature of the book, successful cases of the application of evolutionary algorithms to optimization problems are showcased. These cases provide valuable insights into the practical application of evolutionary algorithms and their effectiveness in addressing complex problems. Additionally, Python source codes are provided, offering users a deeper understanding of the intricacies involved in the practical implementation of evolutionary algorithms.

By leveraging the comprehensive coverage and practical insights provided in this book, individuals can gain the necessary skills and knowledge to apply evolutionary algorithms to their engineering and scientific endeavors, unlocking new opportunities for innovation and progress.

Weight: 536g
Dimension: 159 x 241 x 21 (mm)
ISBN-13: 9780367263133