A theoretical analysis of immune inspired somatic contiguous hypermutations for function optimization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5666 LNCS, Issue , 2009, Pages 80-94

  Jansen, Thomas ^a   Zarges, Christine ^b  

^a UNIVERSITY COLLEGE CORK   (Ireland)

^b TU DORTMUND UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL IMMUNE SYSTEM; FUNCTION OPTIMIZATION; HYPER MUTATION; MUTATION OPERATORS; OPTIMIZATION PROBLEMS; OPTIMIZATION TASK; RIGOROUS ANALYSIS; THEORETICAL INVESTIGATIONS;

EVOLUTIONARY ALGORITHMS; IMMUNOLOGY; MATHEMATICAL OPERATORS;

OPTIMIZATION;

EID: 70350348725     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-03246-2_12     Document Type: Conference Paper

References (21)

1. Aarts, E., Lenstra, J.K. (eds.): Local Search in Combinatorial Optimization. Princeton University Press, Princeton (2003)
2. Bull, P., Knowles, A., Tedesco, G., Hone, A.: Diophantine benchmarks for the B-cell algorithm. In: Bersini, H., Caarneiro, J. (eds.) ICARIS 2006. LNCS, vol. 4163, pp. 267-279. Springer, Heidelberg (2006)
3. Burnet, F.M.: The Clonal Selection Theory of Acquired Immunity. Cambridge University Press, Cambridge (1959)
4. Clark, E., Hone, A., Timmis, J.: A Markov chain model of the b-cell algorithm. In: Jacob, C., et al. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 318-330. Springer, Heidelberg (2005)
5. Cortés, N.C., Coello, C.A.C.: Multiobjective optimization using ideas from the clonal selection principle. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 158-170. Springer, Heidelberg (2003)
6. Cutello, V., Nicosia, G., Pavone, M.: Exploring the capability of immune algorithms: A characterization of hypermutation operators. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 263-276. Springer, Heidelberg (2004)
7. Dasgupta, D., Niño, L.F.: Immunological Computation: Theory and Applications. Auerbach (2008)
8. de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)
9. de Castro, L.N., Zuben, F.J.V.: Learning and optimization using the clonal selection principle. IEEE Trans. on Evolutionary Computation 6(3), 239-251 (2002)
10. de Jong, K.A.: Evolutionary Computation: A Unified Approach. MIT Press, Cambridge (2006)
11. Droste, S., Jansen, T., Wegener, I.: On the analysis of the (1+1) evolutionary algorithm. Theoretical Computer Science 276, 51-81 (2002)
12. Droste, S., Jansen, T., Wegener, I.: Upper and lower bounds for randomized search heuristics in black-box optimization. Theory of Computing Systems 39, 525-544 (2006)
13. Droste, S., Wiesmann, D.: On the design of problem-specific evolutionary algorithms. In: Ghosh, A., Tsutsui, S. (eds.) Advances in Evolutionary Computing, pp. 153-173. Springer, Heidelberg (2003)
14. He, J., Yao, X.: A study of drift analysis for estimating computation time of evolutionary algorithms. Natural Computing 3(1), 21-35 (2004)
15. Jansen, T., Wiegand, R.P.: The cooperative coevolutionary (1+1) EA. Evolutionary Computation 12(4), 405-434 (2004)
16. Kelsey, J., Timmis, J.: Immune inspired somatic contiguous hypermutations for function optimisation. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 207-218. Springer, Heidelberg (2003)
17. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671-680 (1983)
18. Oliveto, P.S., Witt, C.: Simplified drift analysis for proving lower bounds in evolutionary optimization. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 82-91. Springer, Heidelberg (2008)
19. Wegener, I.: Methods for the analysis of evolutionary algorithms on pseudo-boolean functions. In: Sarker, R., et al. (eds.) Evolutionary Optimization. Kluwer Academic Publishers, Dordrecht (2001)
20. Zarges, C.: Rigorous runtime analysis of inversely fitness proportional mutation rates. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 112-122. Springer, Heidelberg (2008)
21. Zarges, C.: On the utility of the population size for inversely fitness proportional mutation rates. In: Garibay, I., et al. (eds.) Foundations of Genetic Algorithms (FOGA), pp. 39-46. ACM Press, New York (2009)