Abstract
In this paper we apply a new evolutive approach for solving the Set Covering Problem. This problem is a reasonably well known NP-complete optimization problem with many real world applications. We use a Cultural Evolutionary Architecture to maintain knowledge of Diversity and Fitness learned over each generation during the search process. Our results indicate that the approach is able to produce competitive results in compare with other approximation algorithms solving a portfolio of test problems taken from the ORLIB.
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Beasley, J.E.: Or-library: distributing test problem by electronic mail. Journal of Operational Research Society 41(11), 1069–1072 (1990), Available at http://people.brunel.ac.uk/mastjjb/jeb/info.html
Beasley, J.E., Chu, P.C.: A genetic algorithm for the set covering problem. European Journal of Operational Research 94(2), 392–404 (1996)
Chu, P.C., Beasley, J.E.: Constraint handling in genetic algorithms: The set partitioning problem. Journal of Heuristics 4(4), 323–357 (1998)
Coello, C.A., Landa, R.: Constrained Optimization Using an Evolutionary Programming-Based Cultural Algorithm. In: Parmee, I. (ed.) Proceedings of the Fifth International Conference on Adaptive Computing Design and Manufacture (ACDM 2002), vol. 5, University of Exeter, Devon, UK, April 2002, pp. 317–328. Springer, Heidelberg (2002)
Crawford, B.: C. Castro. Integrating lookahead and post processing proceures with aco for solving set partitioning and covering problems. In: Rutkowski, L., et al. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 1082–1090. Springer, Heidelberg (2006)
Feo, T., Resende, M.: A probabilistic heuristic for a computationally difficult set covering problem. Operations Research Letters 8, 67–71 (1989)
Gomes, F.C., et al.: Experimental analysis of approximation algorithms for the vertex cover and set covering problems. Comput. Oper. Res. 33(12), 3520–3534 (2006)
Landa, R., Coello, C.A.: Optimization with constraints using a cultured differential evolution approach. In: GECCO ’05: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 27–34. ACM Press, New York (2005)
Landa, R., Coello, C.A.: Use of domain information to improve the performance of an evolutionary algorithm. In: GECCO ’05: Proceedings of the 2005 workshops on Genetic and evolutionary computation, pp. 362–365. ACM Press, New York (2005)
Lozano, M., Herrera, F., Cano, J.R.: Replacement strategies to preserve useful diversity in steady-state genetic algorithms. In: Proceedings of the 8th Online World Conference on Soft Computing in Industrial Applications (September 2003)
Peng, B.: Knowledge and population swarms in cultural algorithms for dynamic environments. PhD thesis, Detroit, MI, USA, Adviser-Robert G. Reynolds (2005)
Reynolds, R.: An introduction to cultural algorithms. In: Third Annual Conference on Evolutionary Programming, pp. 131–139 (1994)
Reynolds, R.G.: Cultural algorithms: theory and applications. In: New ideas in optimization, pp. 367–378. McGraw-Hill, Maidenhead (1999)
Reynolds, R.G., Peng, B.: Cultural algorithms: Modeling of how cultures learn to solve problems. In: ICTAI, pp. 166–172. IEEE Computer Society Press, Los Alamitos (2004)
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Crawford, B., Lagos, C., Castro, C., Paredes, F. (2007). A Cultural Algorithm for Solving the Set Covering Problem. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_41
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DOI: https://doi.org/10.1007/978-3-540-72432-2_41
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