Linear genetic programming using a compressed genotype representation

This paper presents a modularization strategy for linear genetic programming (GP) based on a substring compression/substitution scheme. The purpose of this substitution scheme is to protect building blocks and is in other words a form of learning linkage. The compression of the genotype provides both a protection mechanism and a form of genetic code reuse. This paper presents results for synthetic genetic algorithm (GA) reference problems like SEQ and OneMax as well as several standard GP problems. These include a real world application of GP to data compression. Results show that despite the fact that the compression substrings assumes a tight linkage between alleles, this approach improves the search process.