Speciation Dynamics: Generating Selective Pressure Towards Diversity

Recent approaches in evolutionary robotics (ER) propose to generate behavioral diversity in order to evolve desired behaviors more easily. These approaches require the definition of a behavioral distance which often includes task-specific features and hence a priori knowledge. Alternative methods, that do not explicitly force selective pressure towards diversity (SPTD) but still generate it, are known from the field of artificial life such as artificial ecologies (AE). In this study, we investigate how SPTD is generated without task-specific behavioral features or other forms of a priori knowledge and detect how methods of generating SPTD can be transferred from the domain of AE to ER. A promising finding is that in both types of systems, in systems from ER that generate behavioral diversity and also in the investigated speciation model, selective pressure is generated towards unpopulated regions of search space. We conclude by hypothesizing how knowledge about self-organizing SPTD in AE could be transferred to the domain of ER.