Darwinian Machine Learning: Principles of Machine Learning in Evo-devo, Evo-eco and Evolutionary Transitions in Individuality

Current evolutionary theory describes a Darwinian machine – i.e., heritable variation in reproductive success that assumes fixed mechanisms of variation and selection operating on a fixed reproductive unit. But, in fact, none of these mechanisms is fixed in nature. For example, the distribution of phenotypic variation changes over evolutionary time as a result of the evolution of development, the selective pressures on traits change as a result of the evolution of ecological interactions, and even the identity of the evolutionary unit changes as a result of the evolution of new reproductive strategies and new mechanisms of inheritance. The circular causality implied by an evolutionary process that alters its own mechanisms results in conceptual difficulties and controversies in many areas of evolutionary biology. However, in computer science, the idea that an algorithmic process can improve over time as a function of past experience, including its own past behaviour, has been thoroughly studied in the field of machine learning.