Genetic Algorithm

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

Problem-Based Solution

Solver

Options

Live Editor Tasks

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 .

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.