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About

Michael E. Palmer is a researcher in artificial life, evolutionary robotics, and neural development, whose work bridges biological inspiration and computational design. His most significant contributions center on evolving neural network controllers for physically simulated robots, particularly through novel methods that mimic biological growth processes. Palmer pioneered the use of rule-based systems—inspired by Lindenmayer systems—to "grow" artificial neural networks via evolved neurogenesis and synaptogenesis, allowing neurons to proliferate in 3D space through cell division and functional differentiation. This approach enabled the evolution of controllers for complex, 18-degree-of-freedom robotic "spiders" that could gallop and track compass headings. His landmark 2012 paper introduced an artificial visual cortex, driving behavioral evolution in co-evolved predator and prey robots, demonstrating how sensory-driven neural architectures can emerge through artificial evolution. Though his citation counts are modest (7, 3, and 2 for his top papers), Palmer’s work represents a foundational exploration of how developmental processes can be harnessed to create adaptive robotic intelligence, offering a compelling alternative to hand-coded control systems. His research remains a touchstone for those interested in the intersection of neural development, evolutionary algorithms, and embodied cognition.