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Tytuł artykułu

Evolutionary algorithm to find graph covering subsets using α-cliques

Identyfikatory
Warianty tytułu
Konferencja
Evolutionary Computation and Global Optimization 2006 / National Conference (9 ; 31.05-2.06.2006 ; Murzasichle, Poland)
Języki publikacji
EN
Abstrakty
EN
The article describes a new evolutionary based method to divide graph into strongly connected structures we called α-cliques. The α-clique is a generalization of a clique concept with the introduction of parameter a. Using this parameter it is possible to control the degree (or strength) of connections among vertices (nodes) of this sub-graph structure. The evolutionary approach is proposed as a method that enables to find separate a-cliques that cover the set of graph vertices.
Rocznik
Tom
Strony
351--358
Opis fizyczny
Bibliogr. 16 poz., rys.
Twórcy
autor
autor
Bibliografia
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  • [2] Benson, S.J. and Ye, Y. Approximating maximum stable set and minimum graph coloring problems with the positive semidefinite relaxation. Computational complexity: The problem of approximation, Kluwer Academic Publishers, 2000.
  • [3] Browning, T.R. Applying the Design Structure Matrix to System Decomposition and Integration Problems: A Review and New Directions. IEEE Transactions on Engineering Management, Vol. 48, No 3, 2001.
  • [4] Kloks, T. and Kratsch, D. Listing all minimal separators of a graph. SIAM J. COMPUT., Vol. 27, No. 3, June 1998, pp. 605-613, 1998.
  • [5] Lenkstra, J.K. Sequencing by enumerative methods. In Mathematical Centre Tracts, Amsterdam, 1977.
  • [6] McCulley, C. and Bloebaum, C. A Genetic Tool for Optimal Design Sequencing in Complex Engineering Systems. Structural Optimization, Vol. 12, No. 2-3, pp. 186-201, 1996.
  • [7] Mulawka, J. and Stańczak, J. Genetic Algorithms with Adaptive Probabilities of Operators Selection. In Proceedings of ICCIMA'99, New Delhi, India, pp. 464-468, 1999.
  • [8] Owsiński, J.W. On a new naturally indexed quick clustering method with a global objective function. In Applied Stochastic Models and Data Analysis, Vol. 6, pp. 157-171, 1990.
  • [9] Potrzebowski, H., Stańczak, J. and Sęp, K. Evolutionary method in grouping of units with argument reduction. In Proceedings of the 15th International Conference on Systems Science, Vol. 3, Wrocław, Poland, pp. 29-36, 2004.
  • [10] Potrzebowski, H., Stańczak, J. and Sęp, K. Evolutionary methods in grouping of units. In Proc. of the 4th International Conference on Recognition Systems CORES'05, Springer-Verlag Berlin Heidelberg, pp. 279-286, 2005.
  • [11] Protasi, M. Reactive local search for the maximum clique problem. Algorithmica, vol. 29, no 4, pp. 610-637, 2001.
  • [12] Rogers, J.L. Reducing Design Cycle Time and Cost Thorough Process Resequencing. International Conference on Engineering Design ICED 1997, Tampere, Finland, 1997.
  • [13] Stańczak, J. Rozwój koncepcji i algorytmów dla samodoskonalących się systemów ewolucyjnych, Ph.D. Dissertation, Politechnika Warszawska, 1999.
  • [14] Sysło, M.M., Deo, N. and Kowalik, J. S. Algorithms of discrete optimization, Prentice-Hall, 1983.
  • [15] Wilson, R.J. Introduction to graph theory. Addison Wesley Longman 1996.
  • [16] Yu, T.L., Goldberg, D.E., Yassine, A. and Yassine, C. A Genetic Algorithm Design Inspired by Organizational Theory. Genetic and Evolutionary Computation Conference (GECCO) 2003, Chicago, Illinois, USA, Publ. Springer-Verlag, Heidelberg, Lecture Notes in Computer Science, Vol. 2724/2003, pp. 1620-1621, 2003.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA9-0052-0037
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