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Parameterized Complexity Analysis of Randomized Search Heuristics

  • Frank NeumannEmail author
  • Andrew M. Sutton
Chapter
Part of the Natural Computing Series book series (NCS)

Abstract

This chapter compiles a number of results that apply the theory of parameterized algorithmics to the running-time analysis of randomized search heuristics such as evolutionary algorithms. The parameterized approach articulates the running time of algorithms solving combinatorial problems in finer detail than traditional approaches from classical complexity theory. We outline the main results and proof techniques for a collection of randomized search heuristics tasked to solve NP-hard combinatorial optimization problems such as finding a minimum vertex cover in a graph, finding a maximum leaf spanning tree in a graph, and the traveling salesperson problem.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Optimisation and Logistics Group, School of Computer ScienceThe University of AdelaideAdelaideAustralia
  2. 2.Department of Computer ScienceUniversity of Minnesota DuluthDuluthUSA

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