Zusammenfassung
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
Nutzer