@article{19962, abstract = {{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, which do not explicitly force selective pressure towards diversity (SPTD) but still generate it, are known from the field of artificial life, such as in artificial ecologies (AEs). 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. In a simple case study we investigate the practical implications of these findings and point to options for transferring the idea of self-organizing SPTD in AEs to the domain of ER.}}, author = {{Hamann, Heiko}}, issn = {{1064-5462}}, journal = {{Artificial Life}}, pages = {{464--480}}, title = {{{Lessons from Speciation Dynamics: How to Generate Selective Pressure Towards Diversity}}}, doi = {{10.1162/artl_a_00186}}, year = {{2015}}, } @article{20177, abstract = {{One of the main challenges in automatic controller synthesis is to develop methods that can successfully be applied for complex tasks. The difficulty is increased even more in the case of settings with multiple interacting agents. We apply the artificial homeostatic hormone system (AHHS) approach, which is inspired by the signaling network of unicellular organisms, to control a system of several independently acting agents decentrally. The approach is designed for evaluation-minimal, artificial evolution in order to be applicable to complex modular robotics scenarios. The performance of AHHS controllers is compared with neuroevolution of augmenting topologies (NEAT) in the coupled inverted pendulums benchmark. AHHS controllers are found to be better for multimodular settings. We analyze the evolved controllers with regard to the usage of sensory inputs and the emerging oscillations, and we give a nonlinear dynamics interpretation. The generalization of evolved controllers to initial conditions far from the original conditions is investigated and found to be good. Similarly, the performance of controllers scales well even with module numbers different from the original domain the controller was evolved for. Two reference implementations of a similar controller approach are reported and shown to have shortcomings. We discuss the related work and conclude by summarizing the main contributions of our work.}}, author = {{Hamann, Heiko and Schmickl, Thomas and Crailsheim, Karl}}, issn = {{1064-5462}}, journal = {{Artificial Life}}, number = {{2}}, pages = {{165--198}}, title = {{{A Hormone-Based Controller for Evaluation-Minimal Evolution in Decentrally Controlled Systems}}}, doi = {{10.1162/artl_a_00058}}, volume = {{18}}, year = {{2012}}, }