David E. Moriarty

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About

David E. Moriarty is a pioneering researcher in the field of evolutionary robotics and neuro-evolution, best known for his foundational work on hierarchical and modular approaches to evolving neural networks. His key research areas include evolutionary computation, neural network design, and autonomous robot control. Moriarty’s major contribution is the development of the SANE (Symbiotic, Adaptive Neuro-Evolution) system, which demonstrated that evolving individual neurons—rather than entire networks—can lead to more efficient genetic search and scalable solutions. His seminal 2002 paper, "Hierarchical evolution of neural networks" (60 citations), formalized this concept, laying the groundwork for later advances in modular and hierarchical neuro-evolution. Earlier, his 1996 work, "Evolving Obstacle Avoidance Behavior in a Robot Arm" (39 citations), showcased a novel alternative to supervised learning by using evolutionary algorithms to generate complex, reactive behaviors without explicit examples. This work was among the first to prove that evolution could effectively solve real-world control problems in robotics. Moriarty’s research has been highly influential, inspiring subsequent generations of researchers in evolutionary robotics and autonomous systems, and his contributions remain a cornerstone of the field.