Stochastic algorithms

I. Random search (stochastic algorithms)

Random search

II. Stochatic descent

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)

III. Iterated local search

Iterated local search

IV. Guided local search

Guided local search

VI. Scattered research and taboo research

VII. Sample average approximation and response surface methodoology

VIII. Exercises and Tutorials

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IX. Course Synopsis

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Contenus

Stochastic optimization (artificial intelligence) refers to a set of methods for minimizing or maximizing an objective function with randomness: random search, stochastic descent, local search iterated, guided local search, scattered research, taboo research, 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.