Synopses & Reviews
Bioinspired computation methods such as evolutionary algorithms and ant colony optimization are being applied successfully to complex engineering problems and to problems from combinatorial optimization, and with this comes the requirement to more fully understand the computational complexity of these search heuristics. This is the first textbook covering the most important results achieved in this area. The authors study the computational complexity of bioinspired computation and show how runtime behavior can be analyzed in a rigorous way using some of the best-known combinatorial optimization problems -- minimum spanning trees, shortest paths, maximum matching, covering and scheduling problems. A feature of the book is the separate treatment of single- and multiobjective problems, the latter a domain where the development of the underlying theory seems to be lagging practical successes. This book will be very valuable for teaching courses on bioinspired computation and combinatorial optimization. Researchers will also benefit as the presentation of the theory covers the most important developments in the field over the last 10 years. Finally, with a focus on well-studied combinatorial optimization problems rather than toy problems, the book will also be very valuable for practitioners in this field.
From the reviews: "Bioinspired computing is successful in practice. Over the past decade a body of theory for bioinspired computing has been developed. The authors have contributed significantly to this body and give a highly readable account of it." Kurt Mehlhorn, Max Planck Institute for Informatics, and Saarland University, Germany "Bioinspired algorithms belong to the most powerful methods used to tackle real world optimization problems. This book gives such algorithms a solid foundation. It presents some of the most exciting results that have been obtained in bioinspired computing in the last decade." Zbigniew Michalewicz, University of Adelaide, Australia "This book presents a most welcome theoretical computer science approach and perspective to the design and analysis of discrete evolutionary algorithms. It describes the design and derivation of evolutionary algorithms which have precise computation complexity bounds for combinatorial optimization. The book should appeal to researchers and practitioners of evolutionary algorithms and computation who want to learn the state of the art in evolutionary algorithm theory." Una-May O'Reilly, CSAIL, MIT, USA "The evolutionary computation community has been in need of rigorous results concerning the computational complexity of their approaches for decades. This is the first textbook covering such a fundamental topic. It provides an excellent overview of the state of the art in this research area, in terms of both the results obtained and the analytical methods. It is an indispensable book for everyone who is interested in the foundations of evolutionary computation." Xin Yao, University of Birmingham, UK "This timely book will be useful to many researchers and advanced undergraduate and graduate students. The key strength of the book is the complexity analysis of the algorithms for a variety of combinatorial optimization problems on graphs. Furthermore, it provides a comprehensive treatment of evolutionary algorithms and ant colony optimization. The book is recommended to anyone working in the areas of computational complexity, combinatorial optimization, and engineering." ACM Computing Reviews, Manish Gupta, May 2011 "This book treats bio-inspired computing methods as stochastic algorithms and presents rigorous results on their runtime behavior. The book is meant to give researchers a state-of-the-art presentation of theoretical results on bio-inspired computing methods in the context of combinatorial optimization. It can be used as basic material for courses on bio-inspired computing that are meant for graduate students and advanced undergraduates." (I. N. Katz, Zentralblatt MATH, Vol. 1223, 2011)
This book shows how runtime behavior can be analyzed in a rigorous way and for combinatorial optimization in particular. It presents well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems.
About the Author
Authors have given tutorials on this topic at major international conferences
Table of Contents
Part I, Introduction: Bioinspired Computation.- Methods for Analysis.- Part II, Single-objective Optimization: Minimum Spanning Trees.- Eulerian Cycles.- Shortest Paths.- Maximum Matchings.- Vertex Cover.- Partition.- Part III, Multiobjective Optimization: Making Problems Easier Using Additional Objectives.- Minimum Spanning Trees.- Vertex Cover, Set Cover.- Multiobjective Minimum Spanning Trees