Much of recent machine learning has focused on deep learning, in which neural network
weights are trained through variants of stochastic gradient descent. An alternative approach …

How does the genome encode the form of the organism? What is the nature of this genomic
code? Inspired by recent work in machine learning and neuroscience, we propose that the …

Perhaps the most ambitious scientific quest in human history is the creation of general
artificial intelligence, which roughly means AI that is as smart or smarter than humans. The …

In recent years, the scientific community has increasingly recognized the complex multi-
scale competency architecture (MCA) of biology, comprising nested layers of active …

Canalization, or robustness to genetic or environmental perturbations, is fundamental to
complex organisms. While there is strong evidence for canalization as an evolved property …

Biological genotypes do not code directly for phenotypes; developmental physiology is the
control layer that separates genomes from capacities ascertained by selection. A key aspect …

Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize
existing biological functions, augmenting biology with new-to-nature capabilities, or creating …

Genetic algorithms, computer programs that simulate natural evolution, are increasingly
applied across many disciplines. They have been used to solve various optimisation …

We introduce the framework of reality-assisted evolution to summarize a growing trend
towards combining model-based and model-free approaches to improve the design of …

In this paper, we survey the use of additional biologically inspired mechanisms, principles,
and concepts in the area of evolutionary reinforcement learning (ERL). While recent years …