Created by W.Langdon from gp-bibliography.bib Revision:1.7970
Start from empty code. (1) set up, (2) predict and (3) learn 0:33. Evolve code directly, use only basic maths, 0:59 Gradients are not provided, they must be evolved if they are to be used. Fitness on 100 CIFAR10 binary classification tasks. AutoML-Zero discovers machine learning from the start. Supervised learning of binary classifier. 3:42 many human like algorithms discovered and the authors are able to categorise GP evolved code in modern ML terms. GP like linear GP sequence of instructions? Better than human can be explained in terms of recent published ANN papers/text books 4:28. Noisy ReLU (2010). Evolution at scale, one experiment evaluated one trillion algorithms 6:18 10000 alg per second per CPU core. parallel population Connection to nature 7:05
2021 HUMIES prize giving video https://www.youtube.com/watch?v=jrT0sfq6WjM 43:10 -- 49:59 Many improvements on ANN",
Genetic Programming entries for Esteban Real Chen Liang David So Quoc V Le