School of Computing

Artificial immune systems programming for symbolic regression

Colin G. Johnson

In C. Ryan, T. Soule, M. Keijzer, E. Tsang, R. Poli, and E. Costa, editors, Genetic Programming: 6th European Conference, LNCS 2610, pages 182-196. Springer, April 2003.


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.

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Bibtex Record

author = {Colin G. Johnson},
title = {Artificial Immune Systems Programming for Symbolic Regression},
month = {April},
year = {2003},
pages = {182-196},
keywords = {determinacy analysis, Craig interpolants},
note = {},
doi = {},
url = {},
    publication_type = {inproceedings},
    submission_id = {19789_1063817187},
    ISBN = {3-540-00971-X},
    booktitle = {Genetic Programming: 6th European Conference},
    editor = {C. Ryan and T. Soule and M. Keijzer and E. Tsang and R. Poli and E. Costa},
    series = {LNCS 2610},
    publisher = {Springer},
    refereed = {yes},

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