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.
Live Editor Tasks
|Optimize||Optimize or solve equations in the Live Editor|
Problem-Based Genetic Algorithm
Basic example minimizing a function with multiple minima in the problem-based approach.
Solve a nonlinear problem with nonlinear constraints and bounds using
ga in the problem-based approach.
Example showing how to use problem-based mixed-integer programming in ga, including how to choose from a finite list of values.
To set options in some contexts, map problem-based variables to solver-based using
Genetic Algorithm Optimization Basics
Presents an example of solving an optimization problem using the genetic algorithm.
Shows how to write a fitness function including extra parameters or vectorization.
Shows how to include constraints in your problem.
Shows how to choose input options and output arguments.
Example showing the effect of several options.
This example shows how setting the initial range can lead to a better solution.
Common Tuning Options
MaxGenerations option determines the maximum number of generations the genetic algorithm takes; see Stopping Conditions for the Algorithm.
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 the use of a hybrid function for improving a solution.
Describes cases where hybrid functions are likely to provide greater accuracy or speed.
Mixed Integer Optimization
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
ga from the final
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.
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
Solve a traveling salesman problem using a custom data type.
Optimizing an objective given by the solution to an
serial or parallel.
Genetic Algorithm Background
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.
Explore the options for the genetic algorithm.