Parameterized complexity analysis of randomized search heuristics

Frank Neumann, Andrew M. Sutton

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationNatural Computing Series
PublisherSpringer
Pages213-248
Number of pages36
DOIs
StatePublished - 2020

Publication series

NameNatural Computing Series
ISSN (Print)1619-7127

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