The evolution of hierarchical representations
In the areas of Genetic Algorithms and Artificial Life, genetic material is often represented by fixed-length chromosomes. The simplification of a fixed size, sequential sequence of genes is in accord with the ‘principle of meaningful building blocks’. The principle suggests that epistatically related genes should be positioned close to one another. However, in situations in which gene dependency information cannot be determined a priori, a Genetic Algorithm that uses static list-structured chromosomes will often not work. The problem of determining gene dependencies is itself a search problem, and seems well suited for the application of a Genetic Algorithm. In this paper, we propose a Genetic Algorithm that evolves a hierarchical representation in which gene dependencies and values of a chromosome coevolve.
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