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 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.
In neuroevolution, a genotype is mapped to a neural network phenotype that is evaluated on some task to derive its fitness.
Stanley and Miikkulainen[11] propose a taxonomy for embryogenic systems that is intended to reflect their underlying properties.
The taxonomy identifies five continuous dimensions, along which any embryogenic system can be placed: Examples of neuroevolution methods (those with direct encodings are necessarily non-embryogenic):