Immune Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Petr Musilek and Adriel Lau and Marek Reformat and 
                 Loren Wyard-Scott",
  title =        "Immune Programming",
  journal =      "Information Sciences",
  year =         "2006",
  volume =       "176",
  number =       "8",
  pages =        "972--1002",
  month =        "22 " # apr,
  email =        "",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computing, Immune programming, Artificial immune
                 system, Clonal selection",
  DOI =          "doi:10.1016/j.ins.2005.03.009",
  abstract =     "'Immune Programming', a paradigm in the field of
                 evolutionary computing taking its inspiration from
                 principles of the vertebrate immune system. These
                 principles are used to derive stack-based computer
                 programs to solve a wide range of problems. An antigen
                 is used to represent the programming problem to be
                 addressed and may be provided in closed form or as an
                 input/output mapping. An antibody set (a repertoire),
                 wherein each member represents a candidate solution, is
                 generated at random from a gene library representing
                 computer instructions. Affinity, the fit of an antibody
                 (a solution candidate) to the antigen (the problem), is
                 analogous to shape-complementarity evident in
                 biological systems. This measure is used to determine
                 both the fate of individual antibodies, and whether or
                 not the algorithm has successfully completed. When a
                 repertoire has not yielded affinity relating algorithm
                 completion, individual antibodies are replaced, cloned,
                 or hypermutated. Replacement occurs according to a
                 replacement probability and yields an entirely new
                 randomly-generated solution candidate when invoked.
                 This randomness (and that of the initial repertoire)
                 provides diversity sufficient to address a wide range
                 of problems. The chance of antibody cloning, wherein a
                 verbatim copy is placed in the new repertoire, occurs
                 proportionally to its affinity and according to a
                 cloning probability. The chances of an effective
                 (high-affinity) antibody being cloned is high,
                 analogous to replication of effective pathogen-fighting
                 antibodies in biological systems. Hypermutation,
                 wherein probability-based replacement of the gene
                 components within an antibody occurs, is also performed
                 on high-affinity entities. However, the extent of
                 mutation is inversely proportional to the antigenic
                 affinity. The effectiveness of this process lies in the
                 supposition that a candidate showing promise is likely
                 similar to the ideal solution. underlying immune
                 theories and their computational models. A set of
                 sample problems are defined and solved using the
                 algorithm, demonstrating its effectiveness and
                 excellent convergent qualities. Further, the speed of
                 convergence with respect to repertoire size limitations
                 and probability parameters is explored and compared to
                 stack-based genetic programming algorithms.",
  notes =        "",

Genetic Programming entries for Petr Musilek Adriel Lau Marek Reformat Loren Wyard-Scott