Applied Evolutionary Algorithms for Engineers Using Python

This book meant for those who seek to apply evolutionary algorithms to problems in engineering and science. To this end, it provides the theoretical background necessary to the understanding of the presented evolutionary algorithms and their shortcomings, while also discussing themes that are pivotal to the successful application of evolutionary algorithms to real-world problems. The theoretical descriptions are illustrated with didactical Python implementations of the algorithms, which not only allow readers to consolidate their understanding, but also provide a sound starting point for those intending to apply evolutionary algorithms to optimization problems in their working fields. Python has been chosen due to its widespread adoption in the Artificial Intelligence community. Those familiar with high level languages such as MATLAB(TM) will not have any difficulty in reading the Python implementations of the evolutionary algorithms provided in the book.

Instead of attempting to encompass most of the existing evolutionary algorithms, past and present, the book focuses on those algorithms that researchers have recently applied to difficult optimization problems, such as control problems with continuous action spaces and the training of high-dimensional convolutional neural-networks. The basic characteristics of real-world optimization problems are presented, together with recommendations on its proper application to evolutionary algorithms. The applied nature of the book is reinforced by the presentation of successful cases of the application of evolutionary algorithms to optimization problems. This is complemented by Python source codes, giving users an insight into the idiosyncrasies of the practical application of evolutionary algorithms.

這本書旨在為那些希望將演化算法應用於工程和科學問題的人提供幫助。為此，它提供了理論背景，以便理解所介紹的演化算法及其缺點，同時討論了對於成功應用演化算法於實際問題至關重要的主題。理論描述以教學性的Python實現為例，這不僅讓讀者能夠鞏固他們的理解，還為那些打算在工作領域中應用演化算法於優化問題的人提供了一個良好的起點。由於Python在人工智能社區中被廣泛採用，因此選擇了Python。熟悉MATLAB(TM)等高級語言的人在閱讀本書中提供的演化算法的Python實現時不會遇到任何困難。

本書不試圖涵蓋大部分現有的演化算法，而是專注於研究人員最近應用於困難優化問題的那些算法，例如具有連續動作空間的控制問題和高維卷積神經網絡的訓練。書中介紹了實際優化問題的基本特徵，並提供了對於演化算法的正確應用的建議。書中還展示了演化算法成功應用於優化問題的案例。這些案例還附帶了Python源代碼，讓用戶深入了解演化算法實際應用的特殊性。

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.

Leonardo Azevedo Scardua於2015年從巴西聖保羅大學獲得電機工程博士學位。他在鐵路行業擁有豐富的工程經驗，並在許多鐵路系統的列車交通控制軟體開發中應用人工智慧和優化演算法。他目前在巴西埃斯皮里圖聯邦理工學院的控制工程系任職。