Genetic Algorithm and Direct Search Toolbox - Documentation

Contents

Index

• Getting Started

• Using Direct Search

• Performing a Pattern Search Using the Optimization Tool GUI

Example - A Linearly Constrained Problem

Displaying Plots

Example - Working with a Custom Plot Function

• Performing a Pattern Search from the Command Line

Calling patternsearch with the Default Options

Setting Options for patternsearch at the Command Line

Using Options and Problems from the Optimization Tool

• Pattern Search Examples: Setting Options

Using a Search Method

Mesh Expansion and Contraction

Mesh Accelerator

Setting Tolerances for the Solver

Constrained Minimization Using patternsearch

Vectorizing the Objective and Constraint Functions

• Parallel Computing with Pattern Search

Parallel Pattern Search

Using Parallel Computing with patternsearch

Parallel Search Function

Implementation Issues in Parallel Pattern Search

Parallel Computing Considerations

• Using the Genetic Algorithm

• Genetic Algorithm Optimizations Using the Optimization Tool GUI

Displaying Plots

Example - Creating a Custom Plot Function

Reproducing Your Results

Example - Resuming the Genetic Algorithm from the Final Population

• Using the Genetic Algorithm from the Command Line

Running ga with the Default Options

Setting Options for ga at the Command Line

Using Options and Problems from the Optimization Tool

Reproducing Your Results

Resuming ga from the Final Population of a Previous Run

Running ga from an M-File

• Genetic Algorithm Examples

Improving Your Results

Population Diversity

Fitness Scaling

Reproduction Options

Mutation and Crossover

Setting the Amount of Mutation

Setting the Crossover Fraction

Comparing Results for Varying Crossover Fractions

Example - Global vs. Local Minima

Using a Hybrid Function

Setting the Maximum Number of Generations

Vectorizing the Fitness Function

Constrained Minimization Using ga

• Parallel Computing with the Genetic Algorithm

Parallel Evaluation of Populations

How to Use Parallel Computing with ga

Implementation of Parallel Genetic Algorithm

Parallel Computing Considerations

• Using Simulated Annealing

• Using Simulated Annealing from the Command Line

Running simulannealbnd With the Default Options

Setting Options for simulannealbnd at the Command Line

Reproducing Your Results

Parallel Computing with Simulated Annealing

Simulated Annealing Examples

• Multiobjective Optimization

• What Is Multiobjective Optimization?

• Using gamultiobj

Problem Formulation

Using gamultiobj with Optimization Tool

Example - Multiobjective Optimization

Options and Syntax: Differences With ga

Parallel Computing with gamultiobj

• Options Reference

• Pattern Search Options

Optimization Tool vs. Command Line

Algorithm Settings

Stopping Criteria

Output Function Options

Display to Command Window Options

Vectorize and Parallel Options (User Function Evaluation)

Options Table for Pattern Search Algorithms

• Genetic Algorithm Options

Optimization Tool vs. Command Line

Population Options

Fitness Scaling Options

Selection Options

Reproduction Options

Mutation Options

Crossover Options

Migration Options

Algorithm Settings

Multiobjective Options

Hybrid Function Options

Stopping Criteria Options

Output Function Options

Display to Command Window Options

Vectorize and Parallel Options (User Function Evaluation)

• Simulated Annealing Options

saoptimset At The Command Line

Temperature Options

Algorithm Settings

Hybrid Function Options

Stopping Criteria Options

Output Function Options

Display Options

• Release Notes

Genetic Algorithm and Direct Search Toolbox roadmap page

	Functions:
	By Category
	Alphabetical List



	Documentation Set

	Getting Started
	Introduces Genetic Algorithm and Direct Search Toolbox software and gets you started using it

	User's Guide
	Provides tutorials and comprehensive information about Genetic Algorithm and Direct Search Toolbox software

	Examples in Documentation
	Lists major examples in the Genetic Algorithm and Direct Search Toolbox documentation



	Product Demos

	Genetic Algorithm and Direct Search Toolbox Demos
	Presents a collection of demos that you can run from the Help browser to help you learn the product



	What's New

	Release Notes
	Summarizes new features, bug fixes, upgrade issues, etc.



	Printable (PDF) Documentation on the Web

PDF documents reflect the most recent version of Genetic Algorithm and Direct Search Toolbox software and might describe features not available in earlier versions of the software.
	Genetic Algorithm and Direct Search Toolbox User's Guide
	Genetic Algorithm and Direct Search Toolbox Release Notes

The MathWorks Web Site Resources

	Demos
	MATLAB Central
	Product Page
	Platforms & Requirements

	Related Books
	Related Products
	Technical Support

© 1984-2009- The MathWorks, Inc. - Site Help - Patents - Trademarks - Privacy Policy - Preventing Piracy - RSS