Artificial neural networks (ANNs) are applied to many real-world problems, ranging from
pattern classification to robot control. In order to design a neural network for a particular task …

This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution,
artificial neural networks are trained through evolutionary algorithms, taking inspiration from …

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 …

While the history of machine learning so far largely encompasses a series of problems
posed by researchers and algorithms that learn their solutions, an important question is …

Deep reinforcement learning (RL) has shown impressive results in a variety of domains,
learning directly from high-dimensional sensory streams. However, when neural networks …

In evolutionary computation, the fitness function normally measures progress toward an
objective in the search space, effectively acting as an objective function. Through deception …

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 …

This paper establishes a link between the challenge of solving highly ambitious problems in
machine learning and the goal of reproducing the dynamics of open-ended evolution in …

Creating open-ended algorithms, which generate their own never-ending stream of novel
and appropriately challenging learning opportunities, could help to automate and accelerate …

Two major goals in machine learning are the discovery and improvement of solutions to
complex problems. In this paper, we argue that complexification, ie the incremental …