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https://github.com/aprowe/evo

Genetic Algorithms, in javascript
https://github.com/aprowe/evo

coffeescript evolutionary-algorithm gene-population javascript population simulation

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Genetic Algorithms, in javascript

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README 

        
evoJS

=========

evoJS is a genetic and evolutionary algorithm tool, making it easy to incorporate evolutionary algorithms in your JavaScript.  



Installation

-------------

To install run `npm install evo-js`.



To run on a browser, include `evo.min.js` in your scripts.



Basic Usage

---------

The tool is used through a `population` object. Here is a simple example



```javascript

    // Instantiate a population object

    var population = evo.population();



    var evaluateGenes = function (genes) {

      /* Use genes to return a score */

    }



    // Define a method to evaluate genes and return the fitness

    // 'genes' is an array of floats

    population.on('run', function(genes){

        return evaluateGenes(genes); // Supply this method to evaluate the genes

    });



    // Run with a stopping criteria

    population.run({generations: 10});



    // Get the best genes

    var result = population.bestGenes();

```



Pool Configuration

----------

As an argument to `evo.population` a configuration object can be passed to override the

defaults. Here are the defaults.

```javascript

    var config = {

        genes: 200, // Number of genes for each member

        size: 100, //  Number of members in the gene population

        cross_rate: 0.05, // The frequency of gene "twists" in two parents genes

        mutate_rate: 0.05, // The frequency of mutations in a parent gene

        mutate_amount: 1.0, // The amount a mutated gene can deviate



        // Each generation is made from a mix of different breeding strategies

        // Ratios defines the ratio of each in the next generation

        ratios: {

            top:    0.25,    // Survivors from last generation

            mutate: 0.25,    // Created by randomly altering genes

            cross:  0.25,    // Created from crossing parents

            random: 0.10,    // Random survivors from last generation

            average:0.05,    // Created by averaging parents together

            fresh:  0.10     // new, totally random gene sets

        }

    });



    var population = evo.population(config);

```



Stopping conditions

----------

As an argument to `population.run` a configuration object can be passed to defined the

stopping conditions

```javascript

    var config = {

        iterations: 1000, // How many times run will be called

        generations: 100, // How many generations will be run

        score: 10.0,      // Minimum score to be reached (higher score is better)

        // A while function can be supplied

        // when false is returned sim stops

        while: function(){

            return true;

        }



        // If true, a method will be attempted to stop when the score

        // stops changing across generations

        auto_run: false

    });



    population.run(config);

```



'Manual' simulations

----------

While population provides the run method to run simulations, the gene population can be accessed

manually for custom simulations.

```javascript

    population = evo.population()



    while(population.generation < 100) {

        // Grab next genes

        var genes = population.nextGenes();



        //Evaluate genes

        var score = evaluateGenes(genes);



        //Report genes back to population

        population.report(genes, score);

    }

```

When the gene population is empty, a new generation will be created.



Member construction

----------

You may provide a member constructor to automate gene evaluation, and use a more

object oriented approach

```javascript

    population = evo.population()



    // Supply a 'constructor' to use the genes

    population.on('member', function(genes){

        var member = {

            height: genes[0],

            width: genes[1]

        };

        return member;

    });



    // Run will now give your constructed member instead of a gene list

    population.on('run', function(member){

        return member.height + member.width;

    });

```