Selection
Selection is the way in which a genetic algorithm decides which neural networks will be parents for the new generation. There are a couple of selection methods, however only a few have been integrated until now.
At the moment, there are 3 built-in selection methods:
| Name |
|---|
| selection.POWER |
| selection.FITNESS_PROPORTIONATE |
| selection.TOURNAMENT |
A description on how each of these work is given below
Usage
You can specify your selection method while calling the evolve() function on a
network or when constructing a new instance of the NEAT algorithm:
var myNetwork = new architect.Perceptron(1,1,1);
var myTrainingSet = [{ input:[0], output:[1]}, { input:[1], output:[0]}];
myNetwork.evolve(myTrainingSet, {
generations: 10,
selection: methods.selection.POWER // eg.
});
Next to selection methods, elitism is also built in the NEAT constructor.
Elitism allows a
genetic algorithm to pass on n neural networks with the highest fitness from
the previous generation to the new generation, without any crossover steps in
between. At the moment, elitism is only possible inside a Neat object. They
can be passed on as follows:
var evolution = new Neat({
selection: methods.selection.FITNESS_PROPORTIONATE,
elitism: 5 // amount of neural networks to keep from generation to generation
});
methods.selection.POWER
When using this selection method, a random decimal value between 0 and 1 will
be generated. E.g. 0.5, then this value will get an exponential value, the
default power is 4. So 0.5**4 = 0.0625. This will be converted into an index
for the array of the current population, which is sorted from fittest to worst.
Config:
- methods.selection.POWER.power : default is
4. Increasing this value will increase the chance fitter genomes are chosen.
methods.selection.FITNESS_PROPORTIONATE
This selection method will select genomes with a probability proportionate to their fitness:
Read more about roulette selection here.
methods.selection.TOURNAMENT
This selection method will select a group of genomes from the population randomly,
sort them by score, and choose the fittest individual with probability p, the
second fittest with probability p*(1-p), the third fittest with probability
p*((1-p)^2)and so on. Read more here.
Config:
- methods.selection.TOURNAMENT.size : default is
5. Must always be lower than the population size. A higher value will result in a population that has more equal, but fitter, parents. - methods.selection.TOURNAMENT.probability : default is
0.5. See the explanation above on how it is implemented.