Artificial Immune System Programming for Symbolic Regression

Artificial Immune Systems are computational algorithms which take their inspiration from the way in which natural immune systems learn to respond to attacks on an organism. This paper discusses how such a system can be used as an alternative to genetic algorithms as a way of exploring program-space in a system similar to genetic programming. Some experimental results are given for a symbolic regression problem. The paper ends with a discussion of future directions for the use of artificial immune systems in program induction.

[1]  Julian Francis Miller,et al.  Cartesian genetic programming , 2000, GECCO '10.

[2]  Una-May O'Reilly,et al.  Program Search with a Hierarchical Variable Lenght Representation: Genetic Programming, Simulated Annealing and Hill Climbing , 1994, PPSN.

[3]  Jonathan Timmis,et al.  Artificial immune systems - a new computational intelligence paradigm , 2002 .

[4]  Rogério de Lemos,et al.  Negative Selection: How to Generate Detectors , 2002 .

[5]  R. Poli,et al.  Exact GP schema theory for headless chicken crossover and subtree mutation , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[6]  Peter Ross,et al.  The evolution and analysis of potential antibody library for use in job-shop scheduling , 1999 .

[7]  D. Dasgupta,et al.  Immunity-based systems: a survey , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[8]  Leandro Nunes de Castro,et al.  Artificial Immune Systems: A New Computational Approach , 2002 .

[9]  John A. Clark,et al.  Protocols are programs too: the meta-heuristic search for security protocols , 2001, Inf. Softw. Technol..

[10]  Nichael Lynn Cramer,et al.  A Representation for the Adaptive Generation of Simple Sequential Programs , 1985, ICGA.

[11]  Jonathan Timmis,et al.  AINE: an immunological approach to data mining , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[12]  Terry Jones,et al.  Crossover, Macromutationand, and Population-Based Search , 1995, ICGA.

[13]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[14]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[15]  Jonathan Timmis,et al.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[16]  Conor Ryan,et al.  An Investigation into the Use of Different Search Strategies with Grammatical Evolution , 2002, EuroGP.

[17]  Trevor J. M. Bench-Capon,et al.  An Examination of Some Metaphorical Contexts for Biologically Motivated Computing , 1994, The British Journal for the Philosophy of Science.

[18]  Conor Ryan,et al.  Grammatical evolution , 2001, IEEE Trans. Evol. Comput..

[19]  Paul Helman,et al.  An immunological approach to change detection: algorithms, analysis and implications , 1996, Proceedings 1996 IEEE Symposium on Security and Privacy.

[20]  John R. Koza,et al.  Genetic Programming II , 1992 .

[21]  L.N. de Castro,et al.  An artificial immune network for multimodal function optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[22]  Jeffrey O. Kephart,et al.  An immune system for cyberspace , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[23]  Jerne Nk.  Towards a network theory of the immune system. , 1974 .