%0 Conference Paper %1 Parent:gecco05lbp %A Parent, Johan %A Nowe, Annie %A Defaweux, Anne %B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2005) %C Washington, D.C., USA %D 2005 %E Rothlauf, Franz %K algorithms, blocks building genetic modularisation, modules, programming, %T Addressing the Even-n-parity problem using Compressed Linear Genetic Programming %U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/54-parent.pdf %X 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

@inproceedings{Parent:gecco05lbp, 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}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Washington, D.C., USA}, author = {Parent, Johan and Nowe, Annie and Defaweux, Anne}, biburl = {https://www.bibsonomy.org/bibtex/2ee2f38bbef4b0218e967cb7dcf1e9add/brazovayeye}, booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2005)}}, editor = {Rothlauf, Franz}, interhash = {ef7fced98021ad48dc4ff4e866298751}, intrahash = {ee2f38bbef4b0218e967cb7dcf1e9add}, keywords = {algorithms, blocks building genetic modularisation, modules, programming,}, month = {25-29 June}, 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?}, timestamp = {2008-06-19T17:49:14.000+0200}, title = {Addressing the Even-n-parity problem using Compressed Linear Genetic Programming}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/54-parent.pdf}, year = 2005 }