Evolutionary Developmental Biology Can Serve as the Conceptual Foundation for a New Design Paradigm in Artificial Intelligence
Zeki Doruk Erden, Boi Faltings

TL;DR
This paper proposes a new AI design paradigm inspired by evolutionary developmental biology, aiming to address limitations of current neural network approaches through biologically grounded principles.
Contribution
It introduces a unifying framework for AI based on EDB principles, bridging biology and machine learning, with concrete system designs demonstrating its advantages.
Findings
Developmental principles can resolve AI limitations.
Biologically inspired designs improve learning systems.
Deeper understanding of evolution informs AI architecture.
Abstract
Artificial intelligence (AI), propelled by advancements in machine learning, has made significant strides in solving complex tasks. However, the current neural network-based paradigm, while effective, is heavily constrained by inherent limitations, primarily a lack of structural organization and a progression of learning that displays undesirable properties. As AI research progresses without a unifying framework, it either tries to patch weaknesses heuristically or draws loosely from biological mechanisms without strong theoretical foundations. Meanwhile, the recent paradigm shift in evolutionary understanding -- driven primarily by evolutionary developmental biology (EDB) -- has been largely overlooked in AI literature, despite a striking analogy between the Modern Synthesis and contemporary machine learning, evident in their shared assumptions, approaches, and limitations upon careful…
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Taxonomy
TopicsEvolutionary Algorithms and Applications · Genetics, Bioinformatics, and Biomedical Research
