Gene expression programming
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Gene Expression Programming (GEP) is an evolutionary algorithm that evolves populations of computer programs in order to solve a user defined problem. GEP has similarities, but is distinct from, the evolutionary computational method of Genetic Programming[dubious ]. In Genetic Programming the individuals comprising a population are typically symbolic expression trees; however, the individuals comprising a population of GEP are encoded as linear chromosomes, which are then translated into expression trees. That is, in GEP, the genotype (the linear chromosomes) and the phenotype (the expression trees) have a different encoding structure.
The expression trees are themselves computer programs evolved to solve a particular problem and are selected according to their performance/fitness in solving the problem at hand. After repeated iteration, populations of such computer programs will ideally discover new traits and become better adapted to a particular selection environment. The desired endpoint of the algorithm is that a good solution has been evolved by the evolutionary process.
Cândida Ferreira, the inventor of the technique, claims that GEP surpasses the traditional Genetic Programming approach for a number of benchmark problems by a factor of 100 to 10,000. She attributes the alleged speed increase to the separate genotype/phenotype representation and the inherently multigenic organization of GEP chromosomes.
For further details of GEP see the GEP paper published in Complex Systems, where the algorithm is described and applied to a set of problems including symbolic regression, Boolean concept learning, and cellular automata.
[edit] Further reading
- Ferreira, Cândida (2006). Gene Expression programming: mathematical modeling by an artificial intelligence. Springer-Verlag. ISBN 3-540-32796-7. "Online Edition ISBN 978-3-540-32849-0"
- Ferreira, C. (2002). Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. Portugal: Angra do Heroismo. ISBN 9729589054. http://www.gene-expression-programming.com/GepBook/Introduction.htm.
