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