I use genetic algorithms application. I run a very simple kind of a problem in order
to show a difference that occurs between the genetic algorithms toolbox and the form of the problem I want to set up.
function [x,fval,exitflag,output,population,score] = main()
%% This is an auto generated MATLAB file from Optimization Tool.
nvars = 1;
%% Start with the default options
options = gaoptimset;
%% Modify options setting
options = gaoptimset(options,'Display','iter');
options = gaoptimset(options,'Generations',61,'StallGenLimit',60);
options = gaoptimset(options,'PopulationSize',2);
[x,fval,exitflag,output,population,score] = ga(@opt,nvars,[],[],[],[],[],[],[],[],options);
function [y] = opt(t)
y = abs( t - 1 ) % objective fcn
Outputs:
x = 0.9603
fval = 0.0397
population =
0.9603
0.9603
0.9603
0.9603
score =
0.0397
0.0397
0.0397
0.0397
Concerning Genetic Algorithms
--> A population of individuals is maintained within search space for a
GA, each individual representing a possible solution to a given problem.
Each individual is coded as a finite length vector of components, or
variables, in terms of some alphabet, usually the binary alphabet {0,1}.
To continue the genetic analogy these individuals are likened
to chromosomes and the variables are analogous to genes.
Thus a chromosome (solution) is composed of several genes (variables).
A fitness score is assigned to each solution representing the abilities of an individual to `compete'.
The individual with the optimal (or generally near optimal) fitness score is sought.
The GA aims to use selective `breeding' of the solutions to produce
`offspring' better than the parents by combining information from the chromosomes.
Regarding the Genetic Algorithms theory (-->) I realize, in some way, that the optimtool('ga') isn't appropriate to this occasion.
Is this right, or there are options which can change the toolbox so that can fit to my problem?
Specifically I (-->) would like to create chromosome arrays that correspond, one by one, to the population individuals, respectively. Each chromosome owns its genes related to variables (unrelated to x variable of objective fcn).
Is there any idea?
to show a difference that occurs between the genetic algorithms toolbox and the form of the problem I want to set up.
function [x,fval,exitflag,output,population,score] = main()
%% This is an auto generated MATLAB file from Optimization Tool.
nvars = 1;
%% Start with the default options
options = gaoptimset;
%% Modify options setting
options = gaoptimset(options,'Display','iter');
options = gaoptimset(options,'Generations',61,'StallGenLimit',60);
options = gaoptimset(options,'PopulationSize',2);
[x,fval,exitflag,output,population,score] = ga(@opt,nvars,[],[],[],[],[],[],[],[],options);
function [y] = opt(t)
y = abs( t - 1 ) % objective fcn
Outputs:
x = 0.9603
fval = 0.0397
population =
0.9603
0.9603
0.9603
0.9603
score =
0.0397
0.0397
0.0397
0.0397
Concerning Genetic Algorithms
--> A population of individuals is maintained within search space for a
GA, each individual representing a possible solution to a given problem.
Each individual is coded as a finite length vector of components, or
variables, in terms of some alphabet, usually the binary alphabet {0,1}.
To continue the genetic analogy these individuals are likened
to chromosomes and the variables are analogous to genes.
Thus a chromosome (solution) is composed of several genes (variables).
A fitness score is assigned to each solution representing the abilities of an individual to `compete'.
The individual with the optimal (or generally near optimal) fitness score is sought.
The GA aims to use selective `breeding' of the solutions to produce
`offspring' better than the parents by combining information from the chromosomes.
Regarding the Genetic Algorithms theory (-->) I realize, in some way, that the optimtool('ga') isn't appropriate to this occasion.
Is this right, or there are options which can change the toolbox so that can fit to my problem?
Specifically I (-->) would like to create chromosome arrays that correspond, one by one, to the population individuals, respectively. Each chromosome owns its genes related to variables (unrelated to x variable of objective fcn).
Is there any idea?