This a demonstration of how to find a minimum of a non-smooth

objective function using the Genetic Algorithm (GA) function in the

Genetic Algorithm and Direct Search Toolbox. Traditional derivative-based

optimization methods, like those found in the Optimization Toolbox, are

fast and accurate for many types of optimization problems. These methods

are designed to solve smooth , i.e., continuous and differentiable,

minimization problems, as they use derivatives to determine the direction

of descent. While using derivatives makes these methods fast and

accurate, they often are not effective when problems lack smoothness,

e.g., problems with discontinuous, non-differentiable, or stochastic

objective functions. When faced with solving such non-smooth problems,

methods like the genetic algorithm or the more recently developed pattern

search methods, both found in the Genetic Algorithm and Direct Search

Toolbox, are effective alternatives. -This is a demonstration of how to find a minimum of a non-smooth

　objective function using the Genetic Algorithm (GA) function in the

　Genetic Algorithm and Direct Search Toolbox. Traditional derivative-based

　optimization methods, like those found in the Optimization Toolbox, are

　fast and accurate for many types of optimization problems.　These methods

　are designed to solve　smooth , i.e., continuous and differentiable,

　minimization problems, as they use derivatives to determine the direction

　of descent. While using derivatives makes these methods fast and

　accurate, they often are not effective when problems lack smoothness,

　e.g., problems with discontinuous, non-differentiable, or stochastic

　objective functions. When faced with solving such non-smooth problems,

　methods like the genetic algorithm or the more recently developed pattern

　search methods, both found in the Genetic Algorithm and Direct Search

　Toolbox, are effective alternatives.