Genetic algorithm solves smooth or nonsmooth optimization problems with any types of constraints, including integer constraints. It is a stochastic, population-based algorithm that searches randomly by mutation and crossover among population members.
Presents an example of solving an optimization problem using the genetic algorithm.
Coding and Minimizing a Fitness Function Using the Genetic Algorithm
Shows how to write a fitness function including extra parameters or vectorization.
Constrained Minimization Using the Genetic Algorithm
Shows how to include constraints in your problem.
Shows how to choose input options and output arguments.
Effects of Genetic Algorithm Options
Example showing the effect of several options.
Nonlinear Constraints Using ga
An example showing how to use various types of constraints.
Global vs. Local Minima Using ga
An examples showing how to search for a global minimum.
Set Maximum Number of Generations
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
progress of ga.
Shows the effect of the mutation and crossover parameters
in ga.
Hybrid Scheme in the Genetic Algorithm
Shows the use of a hybrid function for improving a solution.
Describes cases where hybrid functions are likely to provide greater accuracy or speed.
Solve mixed integer programming problems, where some variables must be integer-valued.
Solving a Mixed Integer Engineering Design Problem Using the Genetic Algorithm
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 ga from
the final population.
Shows how to reproduce results by resetting the random seed.
Provides an example of running ga using
a set of parameters to search for the most effective setting.
Use Exported Options and Problems
Shows how to create and use a problem structure or a set of options.
Vectorize the Fitness Function
How to gain speed using vectorized function evaluations.
Shows how to create and use a custom plot function
in ga.
Custom Output Function for Genetic Algorithm
This example shows the use of a custom output function
in ga.
Custom Data Type Optimization Using the Genetic Algorithm
Solve a traveling salesman problem using a custom data type.
Optimizing an objective given by the solution to an
ODE using patternsearch or ga in
serial or parallel.
What Is the Genetic Algorithm?
Introduces the genetic algorithm.
Explains some basic terminology for the genetic algorithm.
How the Genetic Algorithm Works
Presents an overview of how the genetic algorithm works.
Nonlinear Constraint Solver Algorithms
Explains the Augmented Lagrangian Genetic Algorithm (ALGA) and penalty algorithm.
Reproduce Results in Optimization App
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.