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.
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