ALOPEX
ALOPEX (an acronym from ""ALgorithms Of Pattern EXtraction"") is a correlation based machine learning algorithm first proposed by Tzanakou and Harth in 1974. Principle. In machine learning, the goal is to train a system to minimize a cost function or (referring to ALOPEX) a response function. Many training algorithms, such as backpropagation, have an inherent susceptibility to getting "stuck" in local minima or maxima of the response function. ALOPEX uses a cross-correlation of differences and a stochastic process to overcome this in an attempt to reach the absolute minimum (or maximum) of the response function. Method. ALOPEX, in  its simplest form is defined by an updating equation: formula_1 Where: Discussion. Essentially, ALOPEX changes each system variable formula_15 based on a product of: the previous change in the variable formula_16formula_17, the resulting change in the cost function formula_16formula_19, and the learning rate parameter formula_9. Further, to find the absolute minimum (or maximum), the stochastic process formula_21 (Gaussian or other) is added to stochastically "push" the algorithm out of any local minima.