This paper proposes an incremental approach for building solutions using evolutionary computation. It presents a simple evolutionary model called a Transition model in which partial solutions are constructed that interact to provide larger solutions. An evolutionary process is used to merge these partial solutions into a full solution for the problem at hand. The paper provides a preliminary study on the evolutionary dynamics of this model as well as an empirical comparison with other evolutionary techniques on binary constraint satisfaction.

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Abstract-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.

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