遗传算法

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
`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

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 usinggain 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-basedOptimizeLive Editor task and several solvers.
Set Options in Problem-Based Approach Using varindex
设置选项在某些情况下,映射具体问题具体分析variables to solver-based usingvarindex.

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.
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.
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 optimizinggafrom the final population.
Reproduce Results
显示了如何再现效果lts 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
Explore the options for the genetic algorithm.