I am using the genetic algorithm method. But I observe that the MATLAB optimtool('ga') application works in a different way from this I describe here:
Genetic Algorithms Concept
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.
Optimtool application applies the crossover and mutation options on the population which contains the x variables values. Instead, in my concept there is the silent assumption that
I use the crossover and mutation in chromosomes genes. In optimtool there isn't the ability to define several chromosome genes so that can apply the crossover and mutation to these.
Which is the best way to modify the optimtool application so that can work in my concept?
Genetic Algorithms Concept
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.
Optimtool application applies the crossover and mutation options on the population which contains the x variables values. Instead, in my concept there is the silent assumption that
I use the crossover and mutation in chromosomes genes. In optimtool there isn't the ability to define several chromosome genes so that can apply the crossover and mutation to these.
Which is the best way to modify the optimtool application so that can work in my concept?