- author = "Mihai Oltean",
- title = "Multi Expression Programming - an in-depth description",
- howpublished = "arXiv",
- year = "2021",
- month = "29 " # sep,
- note = "Date given as June 4, 2006",
- keywords = "genetic algorithms, genetic programming",
- bibdate = "2021-10-08",
- bibsource = "http://dblp.uni-trier.de/db/journals/corr/corr2110.html#abs-2110-00367",
-
URL = "
https://arxiv.org/abs/2110.00367",
-
DOI = "
doi:10.21203/rs.3.rs-898407/v1",
- size = "28 pages",
-
abstract = "Multi Expression Programming (MEP) is a Genetic
Programming variant that uses a linear representation
of chromosomes.
MEP individuals are strings of genes encoding complex computer programs. When MEP individuals encode expressions, their representation is similar to the way in which compilers translate C or Pascal expressions into machine code.
A unique MEP feature is the ability to store multiple solutions of a problem in a single chromosome. Usually, the best solution is chosen for fitness assignment. When solving symbolic regression or classification problems (or any other problems for which the training set is known before the problem is solved) MEP has the same complexity as other techniques storing a single solution in a chromosome (such as GP, CGP, GEP or GE).
Evaluation of the expressions encoded into an MEP individual can be performed by a single parsing of the chromosome. Offspring obtained by crossover and mutation is always syntactically correct MEP individuals (computer programs). Thus, no extra processing for repairing newly obtained individuals is needed.",
