 

Please inform me of
your algorithms and any other algorithms that should go into this page.
Thanks.
Probability model based heuristics for
optimisation and search problems have become popular recently, particularly in
the community of evolutionary computation. These heuristics use
probability models to guide their search.
From my point of view, the following
algorithms can be classified as probability model based heuristics.

Estimation of Distribution Algorithms (EDAs)
A probability model is
built based on global statistical information extracted from selected
promising solutions. New offspring are then sampled from the model thus
built.
EDA homepage
maintained by P. Larraņaga, et. al


Evolution Strategies (ES) 
At
each generation, new solutions are sampled from a probability model (sampling is
often called mutation). The wellknown rule to update
the model is the 1/5rule.
ES
homepage maintained by I. Rechenberg.

Ant Colony Optimisation (ACO) 
Ant Colony Optimization
simulates the behaviour of real ant colonies. Pheromone values define a
probability model.
ACO homepge
maintained by
M. Dorigo, et. al

Cross Entropy Method (CE) 
CE treats an
optimisation problem as the estimation of probabilities of "rare events"
in complex stochastic networks.
CE homepage
maintained by
PieterTjerk de Boer

Monte Carlo Methods for Optimisation Problem (MCM) 
The most famous instance of
this class is simulated annealing. Based on Monte Carlo strategies, these
algorithms try to sample from the target probability distribution which
characterizes the best area (points).

Learning Automata (LA) 
Learning automata have been
used for solving noisy optimisation problems in machine learning. A simple
version of this algorithm maintains a probability vector at each iteration. An
action is randomly taken based on this probability vector. Then it receives a
stochastic reward from the environment, which is used to update the
probability vector.

Global Optimisation Methods Based on Response Surface
(RS) 
Surrogates for the objective function and constraint functions are built at
each iteration for guiding the further search. Often, a probability of
improvement is also used in the search.

Adaptive Multiresolution Search
Regions of high average
fitness are sampled on average more than other areas by making the sampling
range dependent on the fitness function of the last sample.


