Jack Mario Mingo
Universidad Autónoma de Madrid, Universidad Carlos III de Madrid
Papers
3
Total Citations
7
H-Index
2
About
Jack Mario Mingo is a researcher whose work sits at the intersection of evolutionary computation, reinforcement learning, and autonomous robotics. His research has focused primarily on Grammatical Evolution — an evolutionary algorithm capable of generating programs in any grammar-defined language — and its enhancement through learning mechanisms inspired by biological theories of adaptation. A central contribution of his career has been the development of Grammatical Evolution Guided by Reinforcement (GEGR), a novel framework that enriches the evolutionary process by allowing individuals within a population to learn during their own lifetimes through reinforcement learning. This work led him to rigorously investigate classic evolutionary learning hypotheses, particularly Lamarckism and the Baldwin Effect, examining how lifetime learning can — or cannot — be inherited across generations to accelerate evolution. Mingo extended these foundations into applied robotics, notably exploring vision-based syntactic language games for robot teams using stochastic regular grammars, addressing both fully autonomous and human-supervised scenarios. Though his citation counts remain modest — his most-cited work drawing three citations — his research represents thoughtful, specialized contributions bridging theoretical evolutionary computation with practical multi-agent systems. His body of work offers valuable insights for researchers exploring the synergy between machine learning and evolutionary algorithms in autonomous systems.
Research Focus
Key Achievements
Top Papers
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