- Population-based incremental learning
- Univariate marginal distribution algorithm
- Compact genetic algorithm
- Bayesian optimization algorithm
- Bayesian network algorithm
- Cross-entropy method
- Probabilistic incremental program evolution
- Stochastic hill-climbing with learning vectors of normal distributions algorithm
- Recombination schemes
- Probabilistic model-building genetic algorithms
- Mutual-information-maximizing input clustering
- Self-adaptive evolution strategies
Probabilistic Algorithms are those algorithms that model a problem or search a problem space using an probabilistic model of candidate solutions. Many Metaheuristics and Computational Intelligence algorithms may be considered probabilistic, although the difference with algorithms is the explicit (rather than implicit) use of the tools of probability in problem solving.
Estimation of Distribution Algorithms (EDA) also called Probabilistic Model-Building Genetic Algorithms (PMBGA) are an extension of the field of Evolutionary Computation that model a population of candidate solutions as a probabilistic model. They generally involve iterations that alternate between creating candidate solutions in the problem space from a probabilistic model, and reducing a collection of generated candidate solutions into a probabilistic model.
The model at the heart of an EDA typically provides the probabilistic expectation of a component or component configuration comprising part of an optimal solution. This estimation is typically based on the observed frequency of use of the component in better than average candidate solutions. The probabilistic model is used to generate candidate solutions in the problem space, typically in a component-wise or step-wise manner using a domain specific construction method to ensure validity.