Presents an example of solving an optimization problem using the genetic algorithm.
Shows how to choose input options and output arguments.
An example showing how to use various types of constraints.
An examples showing how to search for a global minimum.
Shows how to adjust the maximum generations option to obtain a better result.
Shows the importance of population diversity, and how to set it.
Describes fitness scaling, and how it affects the
Shows the effect of the mutation and crossover parameters
Shows how to use a hybrid function to obtain a more accurate local minimum.
Solve mixed integer programming problems, where some variables must be integer-valued
Example showing how to use mixed-integer programming in ga, including how to choose from a finite list of values.
Shows how to continue optimizing
the final population.
Shows how to reproduce results by resetting the random seed.
Provides an example of running
a set of parameters to search for the most effective setting.
Shows how to create and use a problem structure or a set of options.
How to gain speed using vectorized function evaluations.
Shows how to create and use a custom plot function
This example shows the use of a custom output function
Optimizing an objective given by the solution to an
serial or parallel.
Introduces the genetic algorithm.
Explains some basic terminology for the genetic algorithm.
Presents an overview of how the genetic algorithm works.
Explains the Augmented Lagrangian Genetic Algorithm (ALGA) and penalty algorithm.
To reproduce the results of the last run of the genetic algorithm, select the Use random states from previous run check box.
Describes the options for the genetic algorithm.