ALOPEX (an abbreviation of "algorithms of pattern extraction") is a correlation based machine learning algorithm first proposed by Tzanakou and Harth in 1974.
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.
ALOPEX, in its simplest form is defined by an updating equation:
\Delta Wij(n)=\gamma \Delta Wij(n-1)\Delta R(n)+ri(n)
where:
n\geq0
\Delta Wij(n)
Wij
n
\Delta R(n)
R,
n
\gamma
(\gamma <0
R,
\gamma >0
R )
ri(n)\sim N(0,\sigma 2)
Essentially, ALOPEX changes each system variable
Wij(n)
\Delta
Wij(n-1)
\Delta
R(n)
\gamma
rij(n)