Analyzing Evolutionary Algorithms The Computer Science Perspective

From the book reviews:

“This book focuses on the theoretical analysis of evolutionary algorithms as one of the randomized algorithms in computer science. … This book serves as a very useful source for researchers who are interested in exploring these challenging topics. … I highly recommend it for anyone who is looking to explore both the theoretical aspects of evolutionary algorithms and the practical aspects of designing more efficient algorithms.” (R. Qu, Interfaces, Vol. 44 (4), July-August, 2014)

“‘Analyzing evolutionary algorithms’ is a beautiful book that has a lot to offer to people with different backgrounds. It not only explains evolutionary algorithms and puts them into relationship with other randomized search algorithms, it also provides detailed information for specialists who want to understand in depth how, why, and when evolutionary algorithms work. … The book is complemented by an extended list of references and suggestions for further reading.” (Manfred Kerber, zbMATH, Vol. 1282, 2014)

“This textbook provides a self-contained introduction into this exciting research subject. It can be used as a course text for advanced undergraduate or graduate levels, and it is at the same time a much welcome reference book for active researchers in this area. … Each chapter is therefore complemented by a remarks section that briefly summarizes the advances in the respective topics. In many cases pointers are given to recent research reports.” (Carola Doerr, Mathematical Reviews, October, 2013)

“Analyzing Evolutionary Algorithms is aimed at evolutionary computation researchers and enthusiasts who are interested in the theoretical analysis of evolutionary algorithms. It will be accessible to post-graduates and advanced undergraduates in mathematics and/or computer science, and generally anyone with a working background in discrete mathematics, algorithms, and basic probability theory. Theoreticians will benefit from this book because it works well as a convenient reference for essential analytical strategies and many up-to-date results.” (Andrew M. Sutton, Genetic Programming and Evolvable Machines, Vol. 14, 2013)