Editing Neuroevolution (section)

==Comparison with gradient descent==
{{further|Gradient descent}}
Most neural networks use gradient descent rather than neuroevolution. However, around 2017 researchers at [[Uber]] stated they had found that simple structural neuroevolution algorithms were competitive with sophisticated modern industry-standard gradient-descent [[deep learning]] algorithms, in part because neuroevolution was found to be less likely to get stuck in local minima. In ''[[Science (magazine)|Science]]'',
journalist Matthew Hutson speculated that part of the reason neuroevolution is succeeding where it had failed before is due to the increased computational power available in the 2010s.<ref>{{cite journal |last1=Hutson |first1=Matthew |title=Artificial intelligence can 'evolve' to solve problems |journal=Science |date=11 January 2018 |doi=10.1126/science.aas9715 }}</ref>

It can be shown that there is a correspondence between neuroevolution and gradient descent.<ref>{{cite journal |last1=Whitelam |first1=Stephen |last2=Selin |first2=Viktor |last3=Park |first3=Sang-Won |last4=Tamblyn |first4=Isaac |title=Correspondence between neuroevolution and gradient descent |journal=Nature Communications |date=2 November 2021 |volume=12 |issue=1 |pages=6317 |doi=10.1038/s41467-021-26568-2 |pmid=34728632 |pmc=8563972 |arxiv=2008.06643 |bibcode=2021NatCo..12.6317W }}</ref>