×

Genetic algorithms for modelling and optimisation. (English) Zbl 1072.92001

Summary: Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by natural evolution. They have been successfully applied to a wide range of real-world problems of significant complexity. This paper is intended as an introduction to GAs aimed at immunologists and mathematicians interested in immunology. We describe how to construct a GA and the main strands of GA theory before speculatively identifying possible applications of GAs to the study of immunology. An illustrative example of using a GA for a medical optimal control problem is provided. The paper also includes a brief account of the related area of artificial immune systems.

MSC:

92B05 General biology and biomathematics
92C30 Physiology (general)
90C59 Approximation methods and heuristics in mathematical programming
92-08 Computational methods for problems pertaining to biology
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] T. Bäck, D.B. Fogel, Z. Michalewicz (Eds.), Handbook of Evolutionary Computation, IOP Publishing, 1997. · Zbl 0883.68001
[2] Bäck, T.; Hoffmeister, F.; Schwefel, H.-P., A survey of evolution strategies, ()
[3] Bocharov, G.; Klenerman, P.; Ehl, S., Modelling the dynamics of LCMV infection in mice: II. compartmental structure and immunopathology, J. theor. biol., 221, 349-378, (2003)
[4] Boyle, J.; Henderson, D.; McCall, J.; McLeod, H.; Usher, J., Exploring novel chemotherapy treatments using the WWW, Internat. J. med. inform., 47, 1-2, 107-114, (1997)
[5] D.F. Brown, S.J. Cuddy, A.B. Garmendia-Doval, J.A.W. McCall, Prediction of permeability in oil-bearing strata using genetic algorithms, Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, July 2000.
[6] Cassidy, J.; McLeod, H., Is it possible to design a logical development plan for an anti-cancer drug?, Pharm. med., 9, 95-103, (1995)
[7] Chakraborty, A.K.; Dustin, M.L.; Shaw, A.S., In silico models for cellular and molecular immunology: successes, promises and challenges, Nat. immunol., 4, 10, 933-936, (2003)
[8] C. Coello, An updated survey of evolutionary multiobjective optimization techniques: state of the art and future trends, Proceedings of the 1999 Congress on Evolutionary Computation, IEEE Service Center, Washington, DC, 1999, pp. 3-13.
[9] Davis, L., Handbook of genetic algorithms, (1991), Van Nostrand Reinhold New York
[10] De Castro, L.N., Artificial immune systems as a novel soft computing paradigm, Soft comput. J., 7, 7, (2003)
[11] Doherty, P.C., Anatomical environment as a determinant in viral immunity, J. immunol., 155, 1023-1027, (1995)
[12] Doherty, P.C.; Hamilton-Easton, A.M.; Topham, D.J.; Riberdy, J.; Brooks, J.W.; Cardin, R.D., Consequences of viral infections for lymphocyte compartmentalization and homeostasis, Semin immunol., 9, 365-373, (1997)
[13] Engelbrecht, A.P., Computational intelligence an introduction, (2002), Wiley New York
[14] S. Forrest, A.S. Perelson, Genetic algorithms and the immune system in: H.-P. Schwefel, R. Maenner (Eds.), Parallel Problem Solving from Nature, Lecture Notes in Computer Science, vol. 496, Springer, 1991, pp. 320-325.
[15] Goldberg, D.E., Genetic algorithms in search, optimization and machine learning, (1989), Addison-Wesley Reading, MA · Zbl 0721.68056
[16] G.R. Harik, F.G. Lobo, D.E. Goldberg, The compact genetic algorithm, in: D.B. Fogel (Ed.), Proceedings of the IEEE Conference on Evolutionary Computation 1998 (ICEG’98) IEEE Service Centre, Piscataway, NJ, 1998, pp. 523-528.
[17] Holland, J.H., Adaptation in natural and artificial systems, (1975), The University of Michigan Press Ann Arbor, MI
[18] Kauffman, S.A.; Weinberger, E.D.; Perelson, A.S., Maturation of the immune response via adaptime walks on affinity landscapes, (), 349-382
[19] Martin, R.; Teo, K., Optimal control of drug administration in cancer chemotherapy, (1994), World Scientific Singapore, New Jersey, London, Hong Kong · Zbl 0870.92006
[20] McCall, J.; Petrovski, A., A decision support system for chemotherapy using genetic algorithms, (), 65-70 · Zbl 0960.68781
[21] Michaelewicz, Z., Genetic algorithms + data structures = evolution programs, (1999), Springer Berlin
[22] Mitchell, M., An introduction to genetic algorithms, (1998), MIT Press Cambridge, MA · Zbl 0906.68113
[23] Mitchell, M.; Forrest, S.; Holland, J.H., The royal road for genetic algorithms: fitness landscapes and GA performance, ()
[24] Mitchell, M.; Holland, J.H.; Forrest, S., When will a genetic algorithm outperform hillclimbing, ()
[25] Mühlenbein, H.; Paaß, G., From recombination of genes to the estimation of distributions I. binary parameters, parallel problem solving from nature, (), 178-187
[26] M. Pelikan, D.E. Goldberg, F.G. Lobo, A Survey of Optimization by Building and using Probabilistic Models, University of Illinois Genetic Algorithms Laboratory, Urbana, IL, IlliGAL Report No. 99018, 1999. · Zbl 0988.90052
[27] Petrovski, A.; McCall, J., Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms, (), 531-545
[28] Prügel-Bennett, A.; Shapiro, J.L., An analysis of genetic algorithms using statistical mechanics, Phys. rev. lett., 72, 9, 1305-1309, (1994)
[29] Starzl, T.E.; Zinkernagel, R.M., Antigen localization and migration in immunity and tolerance, N. engl. J. med., 339, 1905-1913, (1998)
[30] Steel, G., Growth kinetics of tumours, (1977), Clarendon Oxford
[31] Swan, G., Role of optimal control theory in cancer chemotherapy, Math. biosci., 101, 237-284, (1990) · Zbl 0702.92007
[32] Veldhuizen, D.; Lamont, G., Multiobjective evolutionary algorithms: analyzing the state-of-the-art, Evolut. comput., 8, 2, 125-147, (2000)
[33] Vose, M.D., The simple genetic algorithm, (1999), MIT Press Cambridge, MA · Zbl 0952.65048
[34] Wheldon, T., Mathematical models in cancer research, (1988), Adam Hilger Bristol, Philadelphia · Zbl 0696.92002
[35] R.M. Zinkernagel, Immunity to viruses, in: W. Paul (Ed.), Fundamental Immunology, third ed., Raven Press, New York, pp. 1121-1250 (Chapter 34).
[36] Zinkernagel, R.M.; Kelly, C., How antigen influences immunity, Immunologist, 5, 114-120, (1993)
[37] Zitzler, E.; Deb, K.; Thiele, L., Comparison of multi-objective evolutionary algorithms: empirical results, Evolut. comput., 8, 2, 173-195, (2000)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.