- Stochastic descent
- Parallel descent
- Descent several times
- Random descent restart
- Random mutation descent
- Iterative descent
- ES (1 + 1, m, hc)
- Random bit climber
- Genetic algorithm (1 + 1)

Toggle Content

Contenus

Toggle## Stochastic algorithms

Stochastic optimization (artificial intelligence) refers to a set of methods to minimize or maximize an objective function with randomness: random search, stochastic descent, iterated local search, guided local search, dispersed search, taboo search, sample average approximation, response surface methodology.

Stochastic optimization refers to a set of methods for minimizing or maximizing an objective function with randomness. Over the past decades, these methods have become essential tools for science, engineering, business, computing and statistics.

Specific applications are varied, but include: simulations to refine the placement of acoustic sensors, decide when water in a reservoir should be released for hydroelectric power generation, and optimize the parameters of a statistical model for a set of data.