Neuroevolution in Games: State of the Art and Open Challenges

Sebastian Risi, Julian Togelius

Research output: Journal Article or Conference Article in JournalJournal articleResearchpeer-review


This paper surveys research on applying neuroevolution
(NE) to games. In neuroevolution, artificial neural networks
are trained through evolutionary algorithms, taking inspiration
from the way biological brains evolved. We analyse the
application of NE in games along five different axes, which are the
role NE is chosen to play in a game, the different types of neural
networks used, the way these networks are evolved, how the
fitness is determined and what type of input the network receives.
The article also highlights important open research challenges in
the field.
Original languageEnglish
JournalI E E E Transactions on Computational Intelligence and A I in Games
Issue number1
Pages (from-to)25-41
Number of pages17
Publication statusPublished - 2015


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