Abstract
This paper proposes a new genetic algorithm to solve the Optimal Communication Spanning Tree problem. The proposed algorithm works on a tree chromosome without intermediate encoding and decoding, and uses crossovers and mutations which manipulate directly trees, while a traditional genetic algorithm generally works on linear chromosomes. Usually, an initial population is constructed by the standard uniform sampling procedure. But, our algorithm employs a simple heuristic based on Prim’s algorithm to randomly generate an initial population. Experimental results on known data sets show that our genetic algorithm is simple and efficient to get an optimal or near-optimal solution to the OCST problem.
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References
Abuali, F.N., Wainwright, R.L., Schoenefeld, D.A.: Determinant factorization: A new encoding scheme for spanning trees applied to the probabilistic minimum spanning trees problem. In: Proc. 6th Int. Conf. on Genetic Algorithms (ICGA 1995), pp. 470–477. University of Pittsburgh, USA (1995)
Berry, L., Mutagh, B., Sugden, S., McMahon, G.: Application of genetic- based algorithm for optimal design of tree-structured communication networks. In: Proceedings of the Regional Teletraffic Engineering Conference of the International Teletraffic Congress, South Africa, pp. 361–370 (1995)
Crecenzi, P., Kann, V.: A compendium of NP optimization problems. (August 1998), Available online at http://www.nada.kth.se/theory/compendium/
Esbensen, H.: Computing near-optimal solutions to the steiner problem in a graph using a genetic algorithm. Networks 26, 173–185 (1995)
Gibbons, A.: Algorithmic graph theory. Cambridge University Press, New York (1985)
Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)
Hu, T.C.: Optimum communication spanning trees. SIAM J. on Computing, 188–195 (1974)
Johnson, D.S., Lenstra, J.K., Rinnooy Kan, A.H.G.: The complexity of the network design problem. Networks 8, 279–285 (1978)
Julstrom, B.A.: A genetic algorithm for the rectilinear steiner problem. In: Forrest, S. (ed.) Proc. 15th Int. Conf. on Genetic Algorithms, University of Illinois at Urbana-Champaign, pp. 474–479. Morgan Kaufmann, San Mateo (1993)
Kallel, L., Schoenauer, M.: Alternative random initialization in genetic algorithms. In: Proceedings of the Seventh International Conference on Genetic Algorithms, pp. 268–275. Morgan Kaufmann, San Francisco (1997)
Palmer, C.C.: An approach to a problem in network design using genetic algorithms. PhD Thesis, Polytechnic University, Computer Science Department, Brookly, NewYork (1994)
Palmer, C.C., Kershenbaum, A.: An approach to a problem in network design using genetic algorithms. Networks 26, 151–163 (1995)
Peleg, D., Reshef, E.: Deterministic polylog approximation for minimum communication spanning trees. In: Larsen, K.G., Skyum, S., Winskel, G. (eds.) ICALP 1998. LNCS, vol. 1443, pp. 670–679. Springer, Heidelberg (1998)
Radcliffe, N.J., Surry, P.D.: Fundamental limitation on search algorithms: Evolutionary computing in perspective. In: van Leeuwen, J. (ed.) Computer Science Today. LNCS, vol. 1000, pp. 275–291. Springer, Heidelberg (1995)
Sinclair, M.C.: Minimum cost routing and wave-length allocation using a genetic-algorithm/heuristic hybrid approach. In: Proc. 6th IEE Conf. on Telecommuni- cations, Edinburgh, UK, pp. 66–71 (1998)
Surry, P.D., Radcliffe, N.J.: Inoculation to initialise evolutionary search. In: Fogarty, T. (ed.) Evolutionary Computing: AISB Workshop. Springer, Heidelberg (1996)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for search. Technical Report SFI-TR-95-02-010, Santa Fe Institute (1995)
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Li, Y., Bouchebaba, Y. (2000). A New Genetic Algorithm for the Optimal Communication Spanning Tree Problem. In: Fonlupt, C., Hao, JK., Lutton, E., Schoenauer, M., Ronald, E. (eds) Artificial Evolution. AE 1999. Lecture Notes in Computer Science, vol 1829. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10721187_12
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DOI: https://doi.org/10.1007/10721187_12
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