Exact Solutions to the Traveling Salesperson Problem by a Population-Based Evolutionary Algorithm

This articles introduces a (μ + 1)-EA, which is proven to be an exact TSP problem solver for a population of exponential size. We will show non-trivial upper bounds on the runtime until an optimum solution has been found. To the best of our knowledge this is the first time it has been shown that an $\mathcal{NP}$-hard problem is solved exactly instead of approximated only by a black box algorithm.

[1]  Benjamin Doerr,et al.  Crossover can provably be useful in evolutionary computation , 2008, GECCO '08.

[2]  William J. Cook,et al.  The Traveling Salesman Problem: A Computational Study , 2007 .

[3]  William J. Cook,et al.  Implementing the Dantzig-Fulkerson-Johnson algorithm for large traveling salesman problems , 2003, Math. Program..

[4]  Frank Neumann,et al.  Computing minimum cuts by randomized search heuristics , 2008, GECCO.

[5]  Carsten Witt,et al.  Approximating Covering Problems by Randomized Search Heuristics Using Multi-Objective Models , 2007, Evolutionary Computation.

[6]  Yuichi Nagata,et al.  Fast EAX Algorithm Considering Population Diversity for Traveling Salesman Problems , 2006, EvoCOP.

[7]  Marco César Goldbarg,et al.  Particle Swarm for the Traveling Salesman Problem , 2006, EvoCOP.

[8]  Jens Gottlieb,et al.  Evolutionary Computation in Combinatorial Optimization , 2006, Lecture Notes in Computer Science.

[9]  Wolfgang Lenski,et al.  Logic versus Approximation , 2004, Lecture Notes in Computer Science.

[10]  Rajeev Motwani,et al.  Randomized algorithms , 1996, CSUR.

[11]  Thomas C. Rowan Proceedings of the 1961 16th ACM national meeting , 1961 .

[12]  Frank Neumann,et al.  Approximating Minimum Multicuts by Evolutionary Multi-objective Algorithms , 2008, PPSN.

[13]  Simon M. Lucas,et al.  Parallel Problem Solving from Nature - PPSN X, 10th International Conference Dortmund, Germany, September 13-17, 2008, Proceedings , 2008, PPSN.

[14]  Martin Skutella,et al.  Evolutionary algorithms and matroid optimization problems , 2007, GECCO.

[15]  William J. Cook,et al.  The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics) , 2007 .

[16]  Ingo Wegener,et al.  Maximum cardinality matchings on trees by randomized local search , 2006, GECCO.

[17]  Ingo Wegener Randomized Search Heuristics as an Alternative to Exact Optimization , 2004, Logic versus Approximation.

[18]  David S. Johnson,et al.  The Traveling Salesman Problem: A Case Study in Local Optimization , 2008 .

[19]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[20]  Stephanie Forrest,et al.  Genetic algorithms , 1996, CSUR.

[21]  Ingo Wegener,et al.  Evolutionary Algorithms and the Maximum Matching Problem , 2003, STACS.

[22]  Carsten Witt,et al.  UNIVERSITY OF DORTMUND REIHE COMPUTATIONAL INTELLIGENCE COLLABORATIVE RESEARCH CENTER 531 Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Worst-Case and Average-Case Approximations by Simple Randomized Search Heuristics , 2004 .

[23]  M. Held,et al.  A dynamic programming approach to sequencing problems , 1962, ACM National Meeting.

[24]  Ingo Wegener,et al.  Randomized local search, evolutionary algorithms, and the minimum spanning tree problem , 2004, Theor. Comput. Sci..

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

[26]  Ingo Wegener,et al.  The Analysis of Evolutionary Algorithms on Sorting and Shortest Paths Problems , 2004 .