Towards a Theory of Evolution as Multilevel Learning
This work addresses the foundational problem of understanding evolution for theoretical biology and AI, proposing a novel framework but is largely theoretical without empirical validation.
The paper tackles the problem of developing a unified theory of biological evolution, including the origin of life, by applying learning theory to physically renormalizable systems, resulting in the formulation of seven principles that entail major evolutionary features and a generalized version of the Central Dogma of molecular biology.
We apply the theory of learning to physically renormalizable systems in an attempt to develop a theory of biological evolution, including the origin of life, as multilevel learning. We formulate seven fundamental principles of evolution that appear to be necessary and sufficient to render a universe observable and show that they entail the major features of biological evolution, including replication and natural selection. These principles also follow naturally from the theory of learning. We formulate the theory of evolution using the mathematical framework of neural networks, which provides for detailed analysis of evolutionary phenomena. To demonstrate the potential of the proposed theoretical framework, we derive a generalized version of the Central Dogma of molecular biology by analyzing the flow of information during learning (back-propagation) and predicting (forward-propagation) the environment by evolving organisms. The more complex evolutionary phenomena, such as major transitions in evolution, in particular, the origin of life, have to be analyzed in the thermodynamic limit, which is described in detail in the accompanying paper.