遗传算法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.

Nonlinear Constraints Using ga

An example showing how to use various types of constraints.

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

TheMaxGenerationsoption determines the maximum number of generations the genetic algorithm takes; see Stopping Conditions for the Algorithm.

Population Diversity

Shows the importance of population diversity, and how to set it.

Fitness Scaling

Describes fitness scaling, and how it affects the progress ofga.

Vary Mutation and Crossover

Shows the effect of the mutation and crossover parameters inga.

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.

Solving a Mixed Integer Engineering Design Problem Using the Genetic Algorithm

示例显示如何使用整数programming in ga, including how to choose from a finite list of values.

Specialized Tasks

Resume ga

Shows how to continue optimizinggafrom the final population.

Reproduce Results

显示了如何reproduce results by resetting the random seed.

Run ga from a File

Provides an example of runninggausing 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 inga.

Custom Output Function for Genetic Algorithm

This example shows the use of a custom output function inga.

Custom Data Type Optimization Using the Genetic Algorithm

Solve a traveling salesman problem using a custom data type.

Optimize an ODE in Parallel

Optimizing an objective given by the solution to an ODE usingpatternsearchorgain serial or parallel.

遗传算法Background

What Is the Genetic Algorithm?

Introduces the genetic algorithm.

遗传算法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

Explains the Augmented Lagrangian Genetic Algorithm (ALGA) and penalty algorithm.

遗传算法Options

Describes the options for the genetic algorithm.