Global Optimization Toolbox provides methods that search for global solutions to problems that contain multiple maxima or minima. It includes global search, multistart, pattern search, genetic algorithm, and simulated annealing solvers. You can use these solvers to solve optimization problems where the objective or constraint function is continuous, discontinuous, stochastic, does not possess derivatives, or includes simulations or black-box functions with undefined values for some parameter settings.
Genetic algorithm and pattern search solvers support algorithmic customization. You can create a custom genetic algorithm variant by modifying initial population and fitness scaling options or by defining parent selection, crossover, and mutation functions. You can customize pattern search by defining polling, searching, and other functions.
Discover more about Global Optimization Toolbox by exploring these resources.
Explore documentation for Global Optimization Toolbox functions and features, including release notes and examples.
Browse the list of available Global Optimization Toolbox functions.
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Global Optimization Toolbox apps enable you to quickly access common tasks through an interactive interface.
Use Global Optimization Toolbox to solve scientific and engineering challenges: