An Immuno-Genetic Hybrid Algorithm

  • Emad Nabil Misr University for Science and Technology,Information Technology Faculty, Computer Science Department.
  • Amr Badr Cairo University,Computers and Information Faculty, Computer Science Department. 5 Dr. Ahmed Zewail Street, Postal Code: 12613, Orman, Giza, Egypt
  • Ibrahim Farag Amr Badr, Cairo University,Computers and Information Faculty, Computer Science Department. 5 Dr. Ahmed Zewail Street, Postal Code: 12613, Orman, Giza, Egypt


The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this research, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algorithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so that the mutation rates decrease with time where the affinity of the population increases, the hybrid algorithm used for evolving a fuzzy rule system to solve the wellknown Wisconsin Breast Cancer Diagnosis problem (WBCD). Our evolved system exhibits two important characteristics; first, it attains high classification performance, with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is human interpretable. The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. The learning and memory acquisition of our algorithm was verified through its application to a binary character recognition problem. The hybrid algorithm overcomes also GAs and AIS and reached the convergence point before them.


[1] A.P. Engelbrecht, Computational Intelligence: an Introduction, England, John Wiley & Sons; 2003.

[2] C.A. Pena Reyes, M.A. Sipper, Evolving fuzzy rules for breast cancer diagnosis, Proc Nonlinear Theory and Applications, 2, pp 369-372, 1998.

[3] C.A. Pena Reyes, M.A. Sipper, fuzzy-genetic approach to breast cancer diagnosis,Artificial Intelligence in Medicine; vol: 17, num:2, 131-155, 1999.

[4] C.J. Merz, P.M. Murphy, UCI repository of machine learning database, http:/Ėœ learn/MLRepository.html, 1996.

[5] D. Dasgupta , Artificial Immune systems and their applications, Springer-Verlag, inc., 1999.

[6] D. Dasgupta, N. Attoh-Okine, Immunity-Based Systems, IEEE International Conference on Systems, Man, and Cybernetics, Orlando, Florida, pp 363-374, October 12-15,1997.

[7] D.A. Coley, An introduction to genetic algorithms for scientists and engineers, world Scientific Publishing Co.,inc., 2001.

[8] E. Gutuleac, Descriptive Timed Membrane Petri Nets for Modelling of Parallel Computing, International Journal of Computers, Communications & Control, Vol. I, No. S: Suppl. issue, pp. 256-261, 2006.

[9] G. Ciobanu, A Programming Perspective of the Membrane Systems,International Journal of Computers, Communications & Control, Vol. I, No. S: Suppl. issue, pp.13-22, 2006.

[10] H. Zhang, D. Liu, Fuzzy Modeling and Fuzzy Control, Birkhauser, 2006.

[11] J. Rennard, Genetic Algorithm Viewer: Demonstration of a Genetic Algorithm,, 2000.

[12] J.J. Espinosa, J. Vandewalle, Constructing fuzzy models with linguistic Integrity, IEEE Transactions on Fuzzy Systems; vol. 7, no. 4, pp. 377-393, 1999.

[13] L.N. De Castro, Fundamentals of natural computing: basic concepts, algorithms, and applications, CRC Press LLC; 2007.

[14] L.N. De Castro, F.J. Zuben, Artificial Immune Systems: Part I ā€“ Basic Theory and Applications, EEC/Unicamp, Campinas, SP, Tech. Rep. ā€“ RT DCA 01/99, p. 95. 1999.

[15] L.N. De Castro, F.J. Zuben, Learning and optimization using the clonal selection principle ,IEEE transactions on evolutionary computation , vol.:6, num.:3, pp 239-251, Jun, 2002.

[16] L.N. De Castro, F.J. Zuben, The Clonal Selection Algorithm with Engineering Applications, Artificial Immune System Workshop, Genetic and Evolutionary Computation Conference, A. S. Wu (Ed.), pp. 36-37, 2000.

[17] L.N. De Castro, J. Timmis, Artificial Immune Systems (A new computational Approach), Springer - Verlag, 2002.

[18] L.N. De Castro, Natural computing,Information science and technology, Idea Group, Inc., 2005.

[19] R. Setiono, Extracting rules from pruned neural networks for breast cancer diagnosis,Artificial Intelligence in Medicine, vol. 8, no. 1, pp. 37-51, Feb. 1996.

[20] R.R. Yager, L.A. Zadeh, Fuzzy Sets, Neural Networks, and Soft Computing, New York, Van Nostrand Reinhold, 1994.

[21] S. Forrest, S.A. Hofmeyrt, A. Somayajit, Architecture for an Artificial Immune System, Evolutionary Computing, vol. 8, no. 4, pp 443-473, 2000.

[22] T. Back, D. Fogel, Z. Mechalewicz, Glossary, Evolutionary Computation 1: Basic Algorithms and Operators, Institute of Physics Publishing, Bristol and Philadelphia, 2000.

[23] T. Back, The Interaction of Mutation Rate, Selection & Self-Adaptation within a Genetic Algorithm, In Proc. 2nd Int. Conf. on Parallel Problem Solving From Nature, North-Holland, Amsterdam, pp. 85-94, 1992.

[24] W. M. Spears, Adapting Crossover in Genetic Algorithms, Artificial Intelligence Center Internal Report AIC-94-019, Naval Research Laboratory, Washington, DC 20375, 1994.
How to Cite
NABIL, Emad; BADR, Amr; FARAG, Ibrahim. An Immuno-Genetic Hybrid Algorithm. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 4, n. 4, p. 374-385, dec. 2009. ISSN 1841-9844. Available at: <>. Date accessed: 04 july 2020. doi:


genetic algorithms, artificial immune system, fuzzy logic, breast cancer diagnosis, memory acquisition