Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …

Since the mid-1990s, researchers have been trying to use machine-learning-based
approaches to solve a number of different compiler optimization problems. These …

Reinforcement learning is the area of machine learning concerned with learning which
actions to execute in an unknown environment in order to maximize cumulative reward. As …

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 …

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 …

Many traditional reinforcement-learning algorithms have been designed for problems with
small finite state and action spaces. Learning in such discrete problems can been difficult …

Normalization layers and activation functions are fundamental components in deep
networks and typically co-locate with each other. Here we propose to design them using an …

In most modern video games, character behavior is scripted; no matter how many times the
player exploits a weakness, that weakness is never repaired. Yet, if game characters could …

Lifelong learning represents an emerging machine learning paradigm that aims at designing
new methods providing accurate analyses in complex and dynamic real-world …

For domains in which fitness is subjective or difficult to express formally, interactive
evolutionary computation (IEC) is a natural choice. It is possible that a collaborative process …