Abstract

Artificial Embryogeny (AE) can be described as the use of a dynamical system as a mid-step in a design process; Through emulating Biological Embryogenesis, we hope to reach levels of complexity and robustness currently impossible. AE is a new field, and suffers from a lack of standards and meaningful means of evaluation. In this document, we review existing work, discussing motivations and merits of existing approaches. Throughout, we argue that a viewpoint which does not regard environment as a primary source of information risks taking a naïve view of evolution. We argue that "complexity" is vaguely and inconsistently defined, and propose several novel measures; Perhaps the simplest model of AE, the Terminating Cellular Automaton, is introduced, and used to compute and contrast our measures. Next, the Deva family of AE algorithms is introduced, a modular Cellular Automaton-like group. A domain of application from Civil Engineering is chosen as an interpretation of the grown organisms. It is initially shown that it is possible to use a Deva algorithm to evolve Plane 'Fusses successfully, this interpretation providing a discipline-independent measure of success. A series of empirical experiments is undertaken, showing the relative efficacy and effects of several model-level strategies in the context of the evolution of structural design. Finally, we explore the role of environment as a constraint on development of structural form. We demonstrate a strong resistance to environmental change by successfully re-growing the organisms in new environments, showing that some Deva organisms are adding information from the environment to their overall morphology; This provides an artificial analogue to the re-use of genes which characterizes biological development.