Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these search heuristics. This is the first book to explain the most important results achieved in this area.The authors show how runtime behavior can be analyzed in a rigorous way. in particular for combinatorial optimization. They present well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems. Classical single-objective optimization is examined first. They then investigate the computational complexity of bioinspired computation applied to multiobjective variants of the considered combinatorial optimization problems, and in particular they show how multiobjective optimization can help to speed up bioinspired computation for single-objective optimization problems.This book will be valuable for graduate and advanced undergraduate courses on bioinspired computation, as it offers clear assessments of the benefits and drawbacks of various methods. It offers a self-contained presentation, theoretical foundations of the techniques, a unified framework for analysis, and explanations of common proof techniques, so it can also be used as a reference for researchers in the areas of natural computing, optimization and computational complexity. 

Authors have given tutorials on this topic at major international conferences

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