Description: 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.
File list (Check if you may need any files):
webinar_files\smoothFcn.m
.............\fminuncOut.m
.............\fminuncOut1.m
.............\gaplotbestfun.m
.............\nonSmoothFcn.m
.............\nonSmoothOpt.m
.............\PSdemo.m
.............\psOut.m
.............\showNonSmoothFcn.m
.............\showSmoothFcn.m
.............\license.txt
.............\webinar_files\smoothFcn.m
.............\.............\fminuncOut.m
.............\.............\fminuncOut1.m
.............\.............\gaplotbestfun.m
.............\.............\nonSmoothFcn.m
.............\.............\nonSmoothOpt.m
.............\.............\PSdemo.m
.............\.............\psOut.m
.............\.............\showNonSmoothFcn.m
.............\.............\showSmoothFcn.m
.............\webinar_files
webinar_files
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