3 ms·
Gradient descent: Run the neural net, evaluate the fitness function, shift parameters in the direction of the derivative of the fitness function. Genetic algo
by Eliezer 9y ago
Gradient descent: Run the neural net, evaluate the fitness function, shift parameters in the direction of the derivative of the fitness function.
Genetic algorithm: Shift parameters in a batch of random ways, evaluate using fitness function, keep the best.
There is nothing more fundamentally 'creative' about one than the other; these are both just attacks on the problem "here is this function, find parameter settings that get higher scores in it".
- eafan 9y agoAn important difference is the manner by which 'exploration' is codified in evolutionary algorithms, which allows for these algorithms to have a tune-able mix of hill-climbing and niche-finding. RL-based systems, Evolutionary Strategies, Swarm Intelligence, ... all are essentially hill-climbing and are typically much better at the 'exploit' aspect than the 'explore' aspect of the search. When you pair evolutionary algorithms with novelty search, for instance, you get very good coverage, as these guys have done with their sorting network work.