Neuroevolution in games

Neuroevolution - the evolution of weights and/or topology for neural networks - is a common and powerful method in evolutionary robotics and machine learning. In the last decade or so, we have seen a large number of applications of neuroevolution in games. Evolved neural networks have been used to play games, model players, generate content and even enable completely new game genres. To some extent, games seem to be replacing the small mobile robots ubiquitous in evolutionary robotics and simple benchmarks used in reinforcement learning research.

Sebastian Risi and I have written a survey on neuroevolution in games, including a discussion of future research challenges. The main reason is that there was no survey of neuroevolution in games in existence; the other reason was that we wanted a tutorial overview to hand out to the students in our Modern AI for Games course.

A while back we asked the community to send us comments and suggestions for important work we might have overlooked. We received a lot of useful input and incorporated most of the suggested work. Thank you for very much for the help!

Now we are happy to announce that the paper will finally be published in the IEEE Transactions on Computational Intelligence and AI in Games (TCIAIG). We hope you like it!

TCIAIG early access:
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7307180

A preprint of the manuscript is available here:
http://arxiv.org/pdf/1410.7326v3.pdf