Immune Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{musilek:2006:IS,
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author = "Petr Musilek and Adriel Lau and Marek Reformat and
Loren Wyard-Scott",
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title = "Immune Programming",
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journal = "Information Sciences",
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year = "2006",
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volume = "176",
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number = "8",
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pages = "972--1002",
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month = "22 " # apr,
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email = "musilek@ece.ualberta.ca",
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keywords = "genetic algorithms, genetic programming, Evolutionary
computing, Immune programming, Artificial immune
system, Clonal selection",
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DOI = "doi:10.1016/j.ins.2005.03.009",
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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.",
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notes = "http://www.elsevier.com/wps/find/journaldescription.cws_home/505730/description#description",
- }
Genetic Programming entries for
Petr Musilek
Adriel Lau
Marek Reformat
Loren Wyard-Scott
Citations