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 …

Novelty search is a state-of-the-art evolutionary approach that promotes behavioural novelty
instead of pursuing a static objective. Along with a large number of successful applications …

Cooperative coevolutionary algorithms (CCEAs) rely on multiple coevolving populations for
the evolution of solutions composed of coadapted components. CCEAs enable, for instance …

Gamer engagement with computer opponents is an important aspect of computer games.
Players will be bored if computer opponents are predictable, and the game will be …

We present DARLEI, a framework that combines evolutionary algorithms with parallelized
reinforcement learning for efficiently training and evolving populations of UNIMAL agents …

This position article argues that the sciences of the artificial (artificial intelligence and
artificial life) have a special relationship to art that is absent from much of science. Just as art …

Minimal criterion coevolution (MCC) was recently introduced to show that a very simple
criterion can lead to an open-ended expansion of two coevolving populations. Inspired by …

Generalization performance of learning agents depends on the training experience to which
they have been exposed. In game-playing domains, that experience is determined by the …

Search processes guided by objectives are ubiquitous in machine learning. They iteratively
reward artifacts based on their proximity to an optimization target, and terminate upon …

The design of effective, robust and autonomous controllers for multi-agent and multi-robot
systems is a long-standing problem in the fields of computational intelligence and robotics …