Genetic Algorithm

Genetic algorithm solver for mixed-integer or continuous-variable optimization, constrained or unconstrained

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.

Functions

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Problem-Based Solution

`optimvalues`	Create values for optimization problem (Since R2022a)
`solve`	Solve optimization problem or equation problem

Solver

`ga`	Find minimum of function using genetic algorithm

Options

`optimoptions`	Create optimization options
`resetoptions`	Reset options

Live Editor Tasks

Optimize

Optimize or solve equations in the Live Editor (Since R2020b)

Topics

Problem-Based Genetic Algorithm

Minimize Rastrigins' Function Using ga, Problem-Based
Basic example minimizing a function with multiple minima in the problem-based approach.
Constrained Minimization Using ga, Problem-Based
Solve a nonlinear problem with nonlinear constraints and bounds using ga in the problem-based approach.
Solve a Mixed-Integer Engineering Design Problem Using the Genetic Algorithm, Problem-Based
Example showing how to use problem-based mixed-integer programming in ga, including how to choose from a finite list of values.
Feasibility Using Problem-Based Optimize Live Editor Task
Solve a nonlinear feasibility problem using the problem-based Optimize Live Editor task and several solvers.
Set Options in Problem-Based Approach Using varindex
To set options in some contexts, map problem-based variables to solver-based using varindex.

Genetic Algorithm Optimization Basics

Minimize Rastrigin's Function
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.
Options and Outputs
Shows how to choose input options and output arguments.
Effects of Genetic Algorithm Options
Example showing the effect of several options.
Global vs. Local Optimization Using ga
This example shows how setting the initial range can lead to a better solution.

Common Tuning Options

Set Maximum Number of Generations and Stall Generations
Examine the effects of setting the MaxGenerations and MaxStallGenerations options.
Population Diversity
Shows the importance of population diversity, and how to set it.
Fitness Scaling
Describes fitness scaling, and how it affects the progress of ga.
Vary Mutation and Crossover
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.
When to Use a Hybrid Function
Describes cases where hybrid functions are likely to provide greater accuracy or speed.

Mixed Integer Optimization

Mixed Integer ga Optimization
Solve mixed integer programming problems, where some variables must be integer-valued.
Solve 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.

Specialized Tasks

Resume ga
Shows how to continue optimizing ga from the final population.
Reproduce Results
Shows how to reproduce results by resetting the random seed.
Run ga from a File
Provides an example of running ga using a set of parameters to search for the most effective setting.
Vectorize the Fitness Function
How to gain speed using vectorized function evaluations.
Create Custom Plot Function
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.
Optimize ODEs in Parallel
Save time by calling an expensive subroutine just once and computing an ODE solution in parallel using patternsearch or ga.
Optimize Simulink Model in Parallel
This example shows how to optimize a Simulink^® model in parallel using several Global Optimization Toolbox solvers.

Genetic Algorithm Background

What Is the Genetic Algorithm?
Introduces the genetic algorithm.
Genetic Algorithm Terminology
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 for Genetic Algorithm
Explains the Augmented Lagrangian Genetic Algorithm (ALGA) and penalty algorithm.
Genetic Algorithm Options
Explore the options for the genetic algorithm.