Editing Neuroevolution (section)

{{Short description|Form of artificial intelligence}}
{{Distinguish|Evolution of nervous systems|Neural development|Neural Darwinism}}

'''Neuroevolution''', or '''neuro-evolution''', is a form of  [[artificial intelligence]] that uses [[evolutionary algorithm]]s to generate [[artificial neural network]]s (ANN), parameters, and rules.<ref>{{Cite news|url=https://oreilly.com/ideas/neuroevolution-a-different-kind-of-deep-learning|title=Neuroevolution: A different kind of deep learning|last=Stanley|first=Kenneth O.|date=2017-07-13|work=O'Reilly Media|access-date=2017-09-04|language=en}}</ref> It is most commonly applied in [[artificial life]], [[general game playing]]<ref>{{cite journal|last1=Risi |first1=Sebastian|last2=Togelius|first2=Julian |title= Neuroevolution in Games: State of the Art and Open Challenges |journal=IEEE Transactions on Computational Intelligence and AI in Games |volume=9|pages=25–41|year= 2017 |arxiv=1410.7326|doi=10.1109/TCIAIG.2015.2494596|s2cid=11245845}}</ref> and [[evolutionary robotics]]. The main benefit is that neuroevolution can be applied more widely than [[supervised learning]] algorithms, which require a syllabus of correct input-output pairs. In contrast, neuroevolution requires only a measure of a network's performance at a task. For example, the outcome of a game (i.e., whether one player won or lost) can be easily measured without providing labeled examples of desired strategies. Neuroevolution is commonly used as part of the [[reinforcement learning]] paradigm, and it can be contrasted with conventional deep learning techniques that use [[backpropagation]] ([[gradient descent]] on a neural network) with a fixed topology.