A major challenge for evolutionary computation is to evolve phenotypes such as neural
networks, sensory systems, or motor controllers at the same level of complexity as found in …

We present a comprehensive review of the evolutionary design of neural network
architectures. This work is motivated by the fact that the success of an Artificial Neural …

The success of deep learning depends on finding an architecture to fit the task. As deep
learning has scaled up to more challenging tasks, the architectures have become difficult to …

Humans rarely tackle every problem from scratch. Given this observation, the motivation for
this paper is to improve optimization performance through adaptive knowledge transfer …

Traditional optimization solvers tend to start the search from scratch by assuming zero prior
knowledge about the task at hand. Generally speaking, the capabilities of solvers do not …

With the development of deep learning, the design of an appropriate network structure
becomes fundamental. In recent years, the successful practice of Neural Architecture Search …

An important question in neuroevolution is how to gain an advantage from evolving neural
network topologies along with weights. We present a method, NeuroEvolution of …

Research in neuroevolution—that is, evolving artificial neural networks (ANNs) through
evolutionary algorithms—is inspired by the evolution of biological brains, which can contain …

Abstract This paper presents Natural Evolution Strategies (NES), a recent family of black-box
optimization algorithms that use the natural gradient to update a parameterized search …

Natural DNA can encode complexity on an enormous scale. Researchers are attempting to
achieve the same representational efficiency in computers by implementing developmental …