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About

David B. Knoester is a researcher whose work bridges evolutionary biology, artificial intelligence, and robotics, with a focus on understanding and engineering collective behavior. His major contributions lie in exploring how animal grouping behaviors—such as swarming and the selfish herd—are shaped by evolutionary pressures, particularly predation. For instance, his 2016 paper on how predator attack strategies influence the evolution of swarming behavior (39 citations) and his 2013 study on the interplay between density-dependent predation and herd evolution (24 citations) have provided key insights into the adaptive logic of social aggregation. Knoester has also pioneered the use of digital evolution to automate the generation of behavioral models for autonomic systems (2008, 35 citations), demonstrating a novel approach to creating adaptive, self-managing software. Additionally, his work on evolving artificial visual cortices for image recognition (2013, 7 and 6 citations) pushes the boundaries of evolutionary robotics, aiming to equip agents with more sophisticated, biologically-inspired visual processing. With over 100 total citations, Knoester’s research is notable for its interdisciplinary impact, offering both theoretical understanding of natural systems and practical tools for engineering autonomous, intelligent agents.