A fixed budget analysis of randomized search heuristics for the traveling salesperson problem

Randomized Search heuristics are frequently applied to NP-hard combinatorial optimization problems. The runtime analysis of randomized search heuristics has contributed tremendously to their theoretical understanding. Recently, randomized search heuristics have been examined regarding their achievable progress within a fixed time budget. We follow this approach and present a first fixed budget runtime analysis for a NP-hard combinatorial optimization problem. We consider the well-known Traveling Salesperson problem (TSP) and analyze the fitness increase that randomized search heuristics are able to achieve within a given fixed budget.

[1]  Dong Zhou,et al.  The use of tail inequalities on the probable computational time of randomized search heuristics , 2012, Theor. Comput. Sci..

[2]  Frank Neumann,et al.  Randomized Local Search, Evolutionary Algorithms, and the Minimum Spanning Tree Problem , 2004, GECCO.

[3]  Berthold Vöcking,et al.  Worst Case and Probabilistic Analysis of the 2-Opt Algorithm for the TSP , 2007, SODA '07.

[4]  Frank Neumann,et al.  Bioinspired computation in combinatorial optimization: algorithms and their computational complexity , 2010, GECCO '12.

[5]  Giuseppe Cattaneo,et al.  Algorithm engineering , 1999, CSUR.

[6]  Shang-Hua Teng,et al.  Smoothed analysis of algorithms: why the simplex algorithm usually takes polynomial time , 2001, STOC '01.

[7]  Thomas Jansen,et al.  Analysis of evolutionary algorithms: from computational complexity analysis to algorithm engineering , 2011, FOGA '11.

[8]  Anne Auger,et al.  Theory of Randomized Search Heuristics: Foundations and Recent Developments , 2011, Theory of Randomized Search Heuristics.

[9]  Russ Bubley,et al.  Randomized algorithms , 1995, CSUR.

[10]  Howard J. Karloff,et al.  New results on the old k-opt algorithm for the TSP , 1994, SODA '94.

[11]  Xin Yao,et al.  Time complexity of evolutionary algorithms for combinatorial optimization: A decade of results , 2007, Int. J. Autom. Comput..

[12]  Dirk Sudholt,et al.  Analysis of Speedups in Parallel Evolutionary Algorithms for Combinatorial Optimization - (Extended Abstract) , 2011, ISAAC.

[13]  Bodo Manthey,et al.  Smoothed Analysis of the 2-Opt Heuristic for the TSP: Polynomial Bounds for Gaussian Noise , 2013, ISAAC.

[14]  Walter Kern,et al.  A probabilistic analysis of the switching algorithm for the euclidean TSP , 1989, Math. Program..

[15]  Thomas Jansen,et al.  Analyzing Evolutionary Algorithms: The Computer Science Perspective , 2012 .

[16]  Peter Sanders Algorithm Engineering , 2010, Informatik-Spektrum.

[17]  Thomas Jansen,et al.  On the analysis of the (1+1) evolutionary algorithm , 2002, Theor. Comput. Sci..

[18]  JansenThomas,et al.  On the analysis of the (1+ 1) evolutionary algorithm , 2002 .

[19]  Thomas Jansen,et al.  Fixed budget computations: a different perspective on run time analysis , 2012, GECCO '12.

[20]  Thomas Jansen,et al.  A method to derive fixed budget results from expected optimisation times , 2013, GECCO '13.