Parameterized Complexity Analysis of Randomized Search Heuristics

• URL: http://arxiv.org/abs/2001.05120v1
• Date: Wed, 15 Jan 2020 03:43:56 GMT
• Title: Parameterized Complexity Analysis of Randomized Search Heuristics
• Authors: Frank Neumann and Andrew M. Sutton
• Abstract summary: This chapter compiles a number of results that apply the theory of parameterized running-time analysis of randomized algorithms. We outline the main results and proof techniques for a collection of randomized searchs tasked to solve NP-hard optimization problems.
• Score: 16.759797540118665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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