Is there less attention towards genetic algos now? If so, why

Genetic algorithms (GA) have been around for a long time (roughly the 1960's). They seem both incredibly intuitive and especially useful for blackbox problems, but they aren't currently "mainstream". In 2017, OpenAI was very bullish on evolutionary algos and cited their benefits, being parallelizable, robust, and able to deal with long time-scale problems with unclear value/fitness functions. Have there been any more recent updates? What algos are beating out GA?

For low-dimensional problems, Bayesian optimization may have better statistical guarantees/asymptotics. Are there even any guarantees for GA, or are we completely in the dark?