Assessing the performance of two immune inspired algorithms and a hybrid genetic algorithm for function optimisation

Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004

Volumn 1, Issue , 2004, Pages 1044-1051

  Timmis, Jon ^a   Edmonds, Camilla ^a   Kelsey, Johnny ^a  

^a UNIVERSITY OF KENT   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL IMMUNE SYSTEMS (AIS); B-CELL ALGORITHMS (BCA); FUNCTION OPTIMIZATION; HYBRID GENETIC ALGORITHMS;

ANTIBODIES; ANTIGENS; APPROXIMATION THEORY; GENETIC ALGORITHMS; OPTIMIZATION; PROBLEM SOLVING;

IMMUNOLOGY;

EID: 4344595528     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. De Castro, L.N and Von Zuben, F. (2001). "aiNET: An Artificial Immune Network for Data Analysis", in Data Mining: A Heuristic Approach. Abbas, H, Sarker, R and Newton, C (Eds). Idea Group Publishing.
2. De Castro, L.N and Timmis, J. (2002) An Artificial Immune Network for Multimodal Function Optimisation. Proc. Of IEEE World Congress on Evolutionary Computation. Pp. 669-674
3. De Castro, L.N and Timmis, J. (2002) Artificial Immune Systems: A New Computational Intelligence Approach. Springer-Verlag.
4. Timmis, J and Neal, M. (2001). A Resource Limited Artificial Immune System for Data Analysis. Knowledge Based Systems, 14(3-4):121-130.
5. Knight, T and Timmis, J (2001). AINE: An Immunological Approach to Data Mining. In Cercone, N, Lin, T and Wu X. (Eds) IEEE International Conference on Data Mining. Pp. 297-304, San Jose. CA.
6. De Castro, L. N. & Von Zuben, F. J. (2002), Learning and Optimisation Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems, 6(3), pp. 239-251.
7. Jerne, N (1975). Towards a Network theory for the Immune System. Annals of Immunology., Inst. Pasture.
8. Edmonds, C. (2003) Artificial Immune Networks and Multimodal Optimisation. MSc Thesis. University of Kent. Canterbury.UK.
9. Bezerra, B and De Castro, L.N. (2003) Bioinformatics data analysis using an artificial immune network. Proceedings of ICARIS 2003, eds. Timmis, J., Bentley, P. and Hart, E. Lecture Notes in Computer Science 2787, pp. Springer-Verlag, 2003.
10. De Castro, L.N. (2003). The Immune response of an Artificial Immune Network (aiNET). In the proceedings of the Congress on Evolutionary Computation. Pp 1273-1280. Canberra. Australia.
11. Andre, J., Siarry, P. and Dognon, T. (2001). An improvement of the standard genetic algorithm fighting premature convergence in continuous optimisation. Advances in Engineering Software. 32. p. 49-60, 2001.
12. Eiben, A and van Kemenade, C. (1995). Performance of multi-parent crossover operators on numerical function optimization problems Technical Report TR-9533, Leiden University.
13. Kelsey, J and Timmis, J (2003). Immune Inspired Somatic Contiguous Hypermutation. In E. Cantú-Paz et al, editor, Genetic and Evolutionary Computation Conference - GECCO 2003, volume 2723 ofLecture Notes in Computer Science, Chicago. USA., July 2003. Springer-Verlag.
14. Kelsey, J, Timmis, J and Hone, A (2003). Chasing Chaos. In R. Sarker, R. Reynolds, H. Abbass, T. Kay-Chen, R. McKay, D Essam, and T. Gedeon, editors, Proceedings of the Congress on Evolutionary Computation, pages 413-419, Canberra. Australia, December, IEEE.
15. Goldberg, D. and Voessner, S. (1999) optimizing global-local search hybrids. Proceedings of the Genetic and Evolutionary Computation Conference. Pp 220-228. Orlando, Florida, USA
16. Berger, J., Sassi, J and Salois, M. (1999). A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows and Itinerary Constraints. In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 44-51, Orlando, Florida, USA.
17. Nguyen, H. Yoshihara, I., Yamamori, M and Yasunaga, M. (2002) A parallel hybrid genetic algorithm for multiple protein sequence alignment. Proceedings of the 2002 Congress on Evolutionary Computation CEC2002. Pp. 309-314. IEEE Press.
18. Hajela, P., & Yoo, J. S. (1999), "Immune Network Modelling in Design Optimization", In New Ideas in Optimization, D. Corne, M. Dorigo & F. Glover (eds.), McGraw Hill, London, pp. 203-215.
19. Toma, N., Endo, S. & Yamada, K. (1999), "Immune Algorithm with Immune Network and MHC for Adaptive Problem Solving", Proc. of the IEEE System, Man, and Cybernetics, IV, pp. 271-276.
20. Gaspar, A. and Collard, P. (1999) From GAs to Artificial Immune Systems: Improving Adaptation in Time Dependent Optimization. Proceedings of the Congress on Evolutionary Computation. Pp 1859-1866. Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala (Eds).
21. Walker, J and Garrett, S. (2003). Dynamic Function Optimisation: Comparing the Performance of Clonal Selection and Evolutionary Strategies. Lecture Notes in Computer Science 2787. Pages 273-284. Timmis, J., Bentley, P. and Hart, E. (Eds).