Abstract. This paper explores the application of an artificial develop-mental system (ADS) to the field of evolutionary robotics by investi-gating the capability of a gene regulatory network (GRN) to specify a general purpose obstacle avoidance controller both in simulation and on a real robot. Experiments are carried out using the e-puck robot plat-form. It is further proposed to use cross-correlation between inputs and outputs in order to assess the quality of robot controllers more accurately than with observing its behaviour alone. 1

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Abstract—Biological genomes have evolved over a period of millions of years and comprise thousands of genes, even for the simplest organisms. However, in nature, only 1–2% of the genes play an active role in creating and maintaining the organism, while the majority are evolutionary fossils. This raises the question whether a considerably larger number of (partly redundant) genes are required in order to effectively build a functional developmental system, of which, in the final system only a fraction is required for the latter to function. This paper investigates different approaches to creating artificial developmental systems (ADSs) based on variable length gene regulatory networks (GRNs). The GRNs are optimised using an evolutionary algorithm (EA). A comparison is made between the different variable length representations and fixed length representations. It is shown that variable length GRNs can achieve both reducing computational effort during optimisation and increasing speed and compactness of the resulting ADS, despite the higher complexity of the encoding required. The results may also improve the understanding of how to effectively model GRN based developmental systems. Taking results of all experiments into account makes it possible to create an overall ranking of the different patterns used as a testbench in terms of their complexity. This ranking may aid to compare related work against. In addition this allows a detailed assessment of the ADS used and enables the identification of missing mechanisms.