- author = "Johan Parent and Annie Nowe and Anne Defaweux",
- title = "Addressing the Even-n-parity problem using Compressed Linear Genetic Programming",
- booktitle = "Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}",
- year = "2005",
- month = "25-29 " # jun,
- editor = "Franz Rothlauf",
- address = "Washington, D.C., USA",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2005lbp/papers/54-parent.pdf",
- keywords = "genetic algorithms, genetic programming, modules, modularisation, building blocks",
- abstract = "Compressed Linear Genetic Programming (cl-GP) uses substring compression as a modularisation scheme. Despite the fact that the compression of substrings assumes a tight linkage between alleles, this approach improves the GP search process. The compression of the genotype, which is a form of linkage learning, provides both a protection mechanism and a form of genetic code reuse. This text presents the results obtained with the cl-GP on the Even-n-parity problem. Results indicate that the modularization of the cl-GP performs better than a normal l-GP as it allows the cl-GP to preserve useful gene combinations. Additionally the cl-GP modularisation is well suited for problems where the problem size is adjusted in a co-evolutionary setup, the problem size increases each time a solution is found",
-
notes = "Distributed on CD-ROM at GECCO-2005
Pairs of adjacent functions and/or terminals present in large numbers in 10 fit programs may be replaced by a single symbol before crossover and mutation. The intention being to keep them together as a building block.
Representation is a linearised (depth first) tree. Non standard meaning given to {"}co-evolutionary{"}.
Up to even-10-parity evolved (cf \cite{poli:1999:22par} 22 parity). Tight limit on program size. NOOP. Elitism.
Why does size of dictionary rise after generation zero?",
