Computing single source shortest paths using single-objective fitness

  • Authors:

  • Surender Baswana;Somenath Biswas;Benjamin Doerr;Tobias Friedrich;Piyush P. Kurur;Frank Neumann

  • Affiliations:

  • Indian Institute of Technology Kanpur, Kanpur, India;Indian Institute of Technology Kanpur;Max-Planck-Institut für Informatik, Saarbrücken, Germany;International Computer Science Institute, Berkeley, CA, USA;Indian Institute of Technology Kanpur, Kanpur, India;Max-Planck-Institut für Informatik, Saarbrücken, Germany

  • Venue:
  • Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
  • Year:
  • 2009

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Abstract

Runtime analysis of evolutionary algorithms has become an important part in the theoretical analysis of randomized search heuristics. The first combinatorial problem where rigorous runtime results have been achieved is the well-known single source shortest path (SSSP) problem. Scharnow, Tinnefeld and Wegener [PPSN 2002, J. Math. Model. Alg. 2004] proposed a multi-objective approach which solves the problem in expected polynomial time. They also suggest a related single-objective fitness function. However, it was left open whether this does solve the problem efficiently, and, in a broader context, whether multi-objective fitness functions for problems like the SSSP yield more efficient evolutionary algorithms. In this paper, we show that the single objective approach yields an efficient (1+1) EA with runtime bounds very close to those of the multi-objective approach.