Bioinspired Computation in Combinatorial Optimization
Springer Berlin (Verlag)
978-3-642-26584-6 (ISBN)
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.
Authors have given tutorials on this topic at major international conferences
Basics.- Combinatorial Optimization and Computational Complexity.- Stochastic Search Algorithms.- Analyzing Stochastic Search Algorithms.- Single-objective Optimization.- Minimum Spanning Trees.- Maximum Matchings.- Makespan Scheduling.- Shortest Paths.- Eulerian Cycles.- Multi-objective Optimization.- Multi-objective Minimum Spanning Trees.- Minimum Spanning Trees Made Easier.- Covering Problems.- Cutting Problems.
"A very nice and, with respect to the topics treated, a useful contribution to the literature. The book gives a very appealing introduction into the area of bio-inspired algorithms with solid results on the theoretical side, gathering many recent results which so far only have been available in research papers. ... recommendable resource both for researchers who want to learn more on the topic and for preparing a course on bio-inspired algorithms. ... Altogether this is a very recommendable textbook." (Klaus Meer, Mathematical Reviews, February, 2015)
"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." (Manish Gupta, Computing Reviews, 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)
| Erscheint lt. Verlag | 2.1.2013 |
|---|---|
| Reihe/Serie | Natural Computing Series |
| Zusatzinfo | XII, 216 p. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 350 g |
| Themenwelt | Mathematik / Informatik ► Informatik |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Schlagworte | Algorithm analysis and problem complexity • Algorithmen • algorithms • bioinspired computing • combinatorial optimization • Computational Complexity • evolutionary algorithm • evolutionary algorithms • Minimum spanning trees • Multiobjective Optimization • Multi-Objective Optimization • Natural Computing • Optimization • Scheduling |
| ISBN-10 | 3-642-26584-7 / 3642265847 |
| ISBN-13 | 978-3-642-26584-6 / 9783642265846 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
