- Progressive population-based learning
- Compact genetic algorithm
- Probabilistic incremental evolution of the program
- Recombination schemes
- Probabilistic model-building genetic algorithms
- Self-adaptive evolution strategies
Probabilistic algorithms are algorithms that model a problem or find a problem space using a probabilistic model of candidate solutions. Many metaheuristic and computational intelligence algorithms can be considered probabilistic, although the difference to algorithms is the explicit (rather than implicit) use of probability tools 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 estimate 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.