the basic syntax to run the GA in command mode is

x = ga(@fitnessfunction,nvar,options)

fitnessfunction:Function file relating the control variables(x) with function value(F).

nvar: no of control variables

options: Genetic algorithm parameters setting.

__GA Options__

PopulationType: [ 'bitstring' | 'custom' | {'doubleVector'} ]:-Type of the population 'custom' : :binary,'double vector': real values

PopInitRange: [ matrix | {[0;1]} ]: Initial value range default is [0 to 1] you can change this by giving two column vectors .For the example problem discussed for the GUI mode the limits can be like this

l=[0 0]'; u=[10 10]';

PopulationSize: [ positive scalar | {20} ]-Size of the population

EliteCount: [ positive scalar | {2} ]:The number best solution in each generation to be saved.

CrossoverFraction: [ positive scalar | {0.8} ]: cross over value between 0 to 1

MigrationDirection: [ 'both' | {'forward'} ] The solution vector is moved in both direction.(increase or decrease)

MigrationInterval: [ positive scalar | {20} ]

MigrationFraction: [ positive scalar | {0.2} ]

Generations: [ positive scalar | {100} ]: no of iterations

TimeLimit: [ positive scalar | {Inf} ]: Time Limit for the algorithm

FitnessLimit: [ scalar | {-Inf} ]:;Set value or a typical known fitness value

StallGenLimit: [ positive scalar | {50} ]: If Solution is not changing for certain number of generation stop the algorithm

StallTimeLimit: [ positive scalar | {20} ]: If Solution is not changing for certain time, stop the algorithm

InitialPopulation: [ matrix | {[]} ]:You can specify initial feasible solution if already known.

InitialScores: [ column vector | {[]} ]: Known function values

CreationFcn: [ function_handle | {@gacreationuniform} ]; random function for creating initial population

FitnessScalingFcn: [ function_handle | @fitscalingshiftlinear | @fitscalingprop | :testing the fitness

@fitscalingtop | {@fitscalingrank} ]

SelectionFcn: [ function_handle | @selectionremainder | @selectionrandom |

@selectionroulette | @selectiontournament | {@selectionstochunif} ]

CrossoverFcn: [ function_handle | @crossoverheuristic | @crossoverintermediate |

@crossoversinglepoint | @crossovertwopoint | {@crossoverscattered} ]:Different crossovers

MutationFcn: [ function_handle | @mutationuniform | {@mutationgaussian} ]: Mutation function

HybridFcn: [ @fminsearch | @patternsearch | @fminunc | {[]} ]: After GA run another local minimizing function is run

Display: [ off | iter | diagnose | {final} ]: the results are displayed

OutputFcns: [ function_handle | @gaoutputgen | {[]} ]:the output producing function

PlotFcns: [ function_handle | @gaplotbestf | @gaplotbestindiv | @gaplotdistance | @gaplotexpectation | @gaplotgeneology | @gaplotselection | @gaplotrange | @gaplotscorediversity | @gaplotscores | @gaplotstopping | {[]} ]: Choose the graphs you want

PlotInterval: [ positive scalar | {1} ]: The plotting interval

Vectorized: [ 'on' | {'off'} ]

__Program in command mode.__

This file and the function file should be in the same folder which should be default.

the options can be set by the following commands

% setting the genetic algorithm parameters.

options = gaoptimset;

options = gaoptimset('PopulationSize', 50,'Generations', 500,'TimeLimit', 200,'StallTimeLimit',100,

'PlotFcns', {@gaplotbestf,@gaplotbestindiv});

[x ff]=ga(@ex1,2,options)

.

## 26 comments:

thnks

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sir,please post optimal power flow using differential evolution algorithm

sir please post transient stability constrained optimal power flow using differential evolution

sir please help me in optimal power flow using genetic algorithm. can u please email me codes on my email id deepanshu.nimal@gmail.com

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