Using compression to understand the distribution of building blocks in genetic programming populations

Compression algorithms generate a predictive model of data, using the model to reduce the number of bits required to transmit the data (in effect, transmitting only the differences from the model). As a consequence, the degree of compression achieved provides an estimate of the level of regularity in the data. Previous work has investigated the use of these estimates to understand the replication of building blocks within genetic programming (GP) individuals, and hence to understand how different GP algorithms promote the evolution of repeated common structure within individuals. Here, we extend this work to the population level, and use it to understand the extent of similarity between sub-structures within individuals in GP populations.