Abstract
In this work we use a simplified model of the immune system to explore the problem solving feature. We consider only two immunological entities, antigens and antibodies, two parameters, and simple immune operators. The experimental results shows how a simple randomized search algorithm coupled with a mechanism for adaptive recognition of hardest constraints, is sufficient to obtain optimal solutions for any combinatorial optimization problem.
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References
Perelson, A. S., Weisbuch, G., Coutinho, A. Eds.: Theoretical and Experimental Insights into Immunology. New York, NY: Springer-Verlag (1992).
Burnet, F. M.: The Clonal Selection Theory of Acquired Immunity. Cambridge, U.K.: Cambridge Univ. Press (1959).
Forrest, S., Hofmeyr, S. A.: Immunology as Information Processing. Design Principles for Immune System & Other Distributed Autonomous Systems. New York: Oxford Univ. Press, SFI Studies in the Sciences of Complexity (2000).
Nicosia, G., Castiglione, F., Motta, S.: Pattern Recognition by primary and secondary response of an Artificial Immune System. Theory in Biosciences 120 (2001) 93–106.
Dasgupta, D. Ed.: Artificial Immune Systems and their Applications. Berlin, Germany: Springer-Verlag (1998).
Tan, K. C., Lee, T. H., Khor, E. F.: Evolutionary Algorithms with Dynamic Population Size and Local Exploration for Multiobjective Optimization. IEEE Transactions on Evolutionary Computation 5 (2001) 565–588.
Coello Coello, C. A.: An Updated Survey of GA-Based Multiobjective Optimization Techniques. ACM Computing Survey 32 (2000) 109–143.
Garey, M. R., Johnson, D. S.: Computers and Intractability: a Guide to the Theory of NP-completeness. New York: Freeman (1979).
Cutello, V., Mastriani, E., Pappalardo, F.: An Evolutionary Algorithm for the Tconstrained variation of the Minimum Hitting Set Problem. Proc. of the IEEE World Congress on Computational Intelligence. Honolulu, HI (2002).
Mitchell, D., Selman, B., Levesque, H. J.: Hard and easy distributions of SAT problems. Proc. of the AAAI. San Jose, CA (1992) 459–465.
Eiben, A. E., van der Hauw J. K., van Hemert J. I.: Graph coloring with adaptive evolutionary algorithms. J. of Heuristics 4 (1998) 25–46.
Bäck, T., Eiben, A. E., Vink, M. E.: A superior evolutionary algorithm for 3-SAT. Proc. of the 7th Annual Conference on Evolutionary Programming. Lecture Notes in Computer Science 1477 (1998) 125–136.
De Castro, L. N., Von Zuben, F. J.: The Clonal Selection Algorithm with Engineering Applications. Workshop Proc. of the Genetic and Evolutionary Computation Conference (GECCO’00). Las Vegas, NV: Morgan Kaufmann (2000) 36–37.
Forrest, S., Perelson, A., Allen, L., Cherukuri, R.: Self-Nonself discrimination in a computer. Proc. of the IEEE Symposium on Research in Security and Privacy. Oakland, CA: IEEE Press (1994) 202–212.
Rogers, A., Prügel-Bennett, A.: Genetic Drift in Genetic Algorithm Selection Schemes. IEEE Transactions on Evolutionary Computation 3 (1999) 298–303.
Brooks, R.: The relationship between matter and life. Nature 409 (2001) 409–411.
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Cutello, V., Nicosia, G. (2002). An Immunological Approach to Combinatorial Optimization Problems. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_37
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DOI: https://doi.org/10.1007/3-540-36131-6_37
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