Many machine learning methods require non-linear optimization, performed by the backward propagation of model errors, with the process complicated by the presence of multiple minima and saddle points. Numerous gradient descent algorithms are available for optimization, including stochastic gradient descent, conjugate gradient, quasi-Newton and non-linear least squares such as Levenberg-Marquardt. In contrast to deterministic optimization, stochastic optimization methods repeatedly introduce randomness during the search process to avoid getting trapped in a local minimum. Evolutionary algorithms, borrowing concepts from evolution to solve optimization problems, include genetic algorithm and differential evolution.