Grammar-based immune programming

  • Authors:
  • Heder S. Bernardino;Helio J. Barbosa

  • Affiliations:
  • Laboratório Nacional de Computação Científica, Petrópolis, Brazil 25.651-075;Laboratório Nacional de Computação Científica, Petrópolis, Brazil 25.651-075

  • Venue:
  • Natural Computing: an international journal
  • Year:
  • 2011

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Abstract

This paper describes Grammar-based Immune Programming (GIP) for evolving programs in an arbitrary language by immunological inspiration. GIP is based on Grammatical Evolution (GE) in which a grammar is used to define a language and decode candidate solutions to a valid representation (program). However, by default, GE uses a Genetic Algorithm in the search process while GIP uses an artificial immune system. Some modifications are needed of an immune algorithm to use a grammar in order to efficiently decode antibodies into programs. Experiments are performed to analyze algorithm behavior over different aspects and compare it with GEVA, a well known GE implementation. The methods are applied to identify a causal model (an ordinary differential equation) from an observed data set, to symbolically regress an iterated function f(f(x)) = g(x), and to find a symbolic representation of a discontinuous function.