Genetischer Algorithmus
Genetischer Algorithmus-Solver für gemischt-ganzzahlige oder kontinuierliche Variablenoptimierung, eingeschränkt oder uneingeschränkt
Genetische Algorithmen lösen glatte und nicht glatte Optimierungsprobleme mit allen Arten von Einschränkungen, einschließlich ganzzahliger Einschränkungen. Es handelt sich um einen stochastischen, populationsbasierten Algorithmus, der zufällig nach Mutationen und Crossovers unter den Populationsmitgliedern sucht.
Funktionen
Live Editor Tasks
| Optimieren | Optimize or solve equations in the Live Editor |
Themen
Problembasierter genetischer Algorithmus
- 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 inga, 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 usingvarindex.
Grundlagen der genetischen Algorithmusoptimierung
- 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.
Allgemeine Tuning-Optionen
- Set Maximum Number of Generations and Stall Generations
Examine the effects of setting theMaxGenerationsandMaxStallGenerationsoptions. - 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.
Gemischt-ganzzahlige Optimierung
- 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.
Spezialisierte Aufgaben
- Resume ga
Shows how to continue optimizinggafrom the final population. - Reproduce Results
Shows how to 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 ODEs in Parallel
Save time by calling an expensive subroutine just once and computing an ODE solution in parallel usingpatternsearchorga.
Hintergrund genetischer Algorithmen
- 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.
