GEATbx:
Main page
Tutorial
Algorithms
M-functions
Parameter/Options
Example functions
www.geatbx.com
Table of Contents of GEATbx Documentation
GEATbx Introduction
Evolutionary Algorithms: Overview, Methods and Operators
1 Introduction
2 Overview
2.1 Selection
2.2 Recombination
2.3 Mutation
2.4 Reinsertion
2.5 Population models - parallel implementation of evolutionary algorithms
2.6 Application of multiple/different strategies and competition between subpopulations
3 Selection
3.1 Rank-based fitness assignment
3.2 Roulette wheel selection
3.3 Stochastic universal sampling
3.4 Local selection
3.5 Truncation selection
3.6 Tournament selection
3.7 Comparison of selection schemes
4 Recombination
4.1 All representations - Discrete recombination
4.2 Real valued recombination
4.2.1 Intermediate recombination
4.2.2 Line recombination
4.2.3 Extended line recombination
4.3 Binary valued recombination (crossover)
4.3.1 Single-point / double point / multi-point crossover
4.3.2 Uniform crossover
4.3.3 Shuffle crossover
4.3.4 Crossover with reduced surrogate
5 Mutation
5.1 Real valued mutation
5.2 Binary mutation
5.3 Real valued mutation with adaptation of step-sizes
6 Reinsertion
6.1 Global reinsertion
6.2 Local reinsertion
7 Population models - Parallel implementations
7.1 Global model - worker/farmer
7.2 Local model - Diffusion model
7.3 Regional model - Migration
8 Application of different strategies
8.1 Different strategies for each subpopulation
8.1.1 Order of Subpopulations
8.2 Competition between subpopulations
8.2.1 Division of Resources
8.2.2 Distribution of Resources
8.2.3 Resource Consumption
8.2.4 Competition Interval and Competition Rate
8.2.5 Competition Selection
8.2.6 Subpopulation Minimum
8.3 Application of Different Strategies
8.4 Application of Competing Subpopulations
8.5 Conclusion
9 Reference
9.1 Evolutionary Algorithms
9.2 Population models and parallel EA
9.3 Combinatorial optimization
9.4 Visualization
9.5 Polyploidy and Evolutionary Algorithms
9.6 Biology, Genetics and Population genetics
GEATbx Tutorial
1 Introduction
2 Quick Start
2.1 First demonstration
2.2 Second demonstration
2.3 Your first optimization of an own objective function
2.4 Further Steps
3 Writing Objective Functions
3.1 Parametric optimization functions
3.2 Defining default values of the objective function
3.3 Optimization of dynamic systems
3.4 Remark
4 Variable Representation
5 Overview of GEA Toolbox Structure
5.1 Naming Convention
5.2 Calling Tree
5.3 Demo / Startup function
5.4 Toolbox functions (Predefined algorithms)
5.5 Evolutionary Algorithm - Main function
5.5.1 Initialization
5.5.2 Generational loop of the EA
5.5.3 Fitness assignment by ranking
5.5.4 Selection
5.5.5 Recombination/Crossover
5.5.6 Mutation
5.5.7 Evaluation
5.5.8 Reinsertion
5.5.9 Migration
5.5.10 Competition
5.5.11 Visualization
5.6 Utility functions
6 Data Structures of the GEATbx
6.1 Chromosomes (genotype / individuals)
6.2 Phenotypes (decision variables / individuals)
6.3 Objective function values
6.4 Fitness values
6.5 Multiple subpopulations
7 How to Approach new Optimization Problems
7.1 Classifying the Problem and Defining the Objective Function
7.2 Investigating the System Behavior
One and Two-dimensional Slices (Variational Diagrams)
Multi-dimensional Visualization
Decreasing the System Size/Dimension
7.3 Selecting the Optimization Method
7.4 Executing and Evaluating Optimizations
GEATbx Options
Parameter Options
1 Introduction
1.1 Predefined Evolutionary Algorithms
1.2 Option handling with geaoptset.m
Default Options
Define and/or Add Options
Merge Option Structures
Check Validity of Option Structures
1.3 Examples of option settings
1.4 Status and result output during optimization
1.5 Description of Options
2 General options
NumberSubpopulation
NumberIndividuals
VariableFormat
3 Selection options
Selection.Name
Selection.Pressure
Selection.GenerationGap
Selection.ReinsertionRate
Selection.RankingMethod
Selection.RankingMultiobj
Selection.ReinsertionMethod
Selection.LocalDimension
Selection.LocalTopology
Selection.LocalDistance
4 Recombination options
Recombination.Name
Recombination.Rate
5 Mutation options
Mutation.Name
Mutation.Rate
Mutation.Range
Mutation.Precision
6 Migration options
Migration.Do
Migration.Interval
Migration.Rate
Migration.Topology
Migration.Selection
7 Competition options
Competition.Do
Competition.Interval
Competition.Rate
Competition.SubpopMinimum
8 Termination options
Termination.Method
Termination.MaxGenerations
Termination.MaxTime
Termination.Diff2Optimum
Termination.RunningMean
Termination.StdObjV
Termination.GoodWorstObjV
Termination.Phi
Termination.Kappa
Termination.Cluster
9 Output and Visualization options
Output.TextInterval
Output.GrafikInterval
Output.GrafikMethod
Output.GrafikStyle
Output.SaveTextInterval
Output.SaveTextFileName
Output.SaveBinDataInterval
Output.SaveBinDataFileName
Output.StatePlotInterval
Output.StatePlotFunction
Output.TextExclude* and Output.SaveTextExclude*
10 Result and run time options
Run.BestObjectiveValue
Run.CountObjFun
Run.Generation
Run.DoTerminate
11 Objective function options
System.ObjFunFilename
System.ObjFunVarBounds
System.ObjFunAddPara
System.ObjFunVarBoundOut
System.ObjFunGoals
System.ObjFunMinimum
System.ObjFunDescription
12 Special initialization options
Special.InitPresetKeep
Special.InitUniformCreate
Special.InitPresetRand
Special.InitDo
Special.InitFunction
Special.CollectBest.Interval
Special.CollectBest.Rate
Special.CollectBest.Compare
Special.CollectBest.WriteFile
Special.CollectBest.FileName
13 Comparison of Options (1.9x / 2.x / 3.x)
Index
GEATbx:
Main page
Tutorial
Algorithms
M-functions
Parameter/Options
Example functions
www.geatbx.com
This document is part of
version 3.5
of the
GEATbx: Genetic and Evolutionary Algorithm Toolbox for use with Matlab -
www.geatbx.com
.
The Genetic and Evolutionary Algorithm Toolbox is
not public domain
.
© 1994-2004 Hartmut Pohlheim, All Rights Reserved, (
support@geatbx.com
).