Addressing the Even-n-parity problem using Compressed Linear Genetic Programming

Compressed Linear Genetic Programming (cl-GP) uses substring compression as a modularization 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 modularization 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.