Hallauer, S and Nitschke, G (2020) The Expense of Neuro-Morpho Functional Machines, Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2020).
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Abstract
An unsolved problem in both natural and artificial evolutionary systems is determining the exact environmental and evolutionary conditions that enable complexity to evolve. This is especially pertinent in evolutionary robotics where possible problem-solving behavior is constrained by brain (controller) and body (morphology) complexity. We evaluate the impact of environments and complexity costs on robotic controller and morphology evolution across various evolutionary robotics task scenarios. This study uses evolutionary robotics as an experimental platform to investigate the arrow of complexity hypothesis, previously demonstrated to hold in artificial evolutionary systems given an imposed complexity cost. Specifically, we test whether energy costs imposed on evolving robot controller and morphology complexity enables the evolution of increasingly complex controller and morphological designs concomitant with increasing environment complexity. Morphological complexity was equated with possible sensor configurations for a physical counterpart Khepera III mobile robot and neural complexity was equated to artificial neural network topological configurations that coupled with a robot’s evolved morphology.
| Item Type: | Conference paper |
|---|---|
| Subjects: | Computing methodologies > Machine learning |
| Date Deposited: | 03 Dec 2021 11:13 |
| Last Modified: | 03 Dec 2021 11:13 |
| URI: | https://pubs.cs.uct.ac.za/id/eprint/1483 |
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