Evo for Meteor

As a general purpose, tree-based genetic algorithm framework, Evo provides methods of object mutation, crossover, and selection. This smart package implements these features in an accessible fashion while taking advantage of Meteor's reactive functionality & collections.

Checklist

######Evo is fully functional; however, I'd like to do some fine tuning before promoting it on Atmosphere. Here's a rough overview:

• Implement Evolutionary Primitives from Evo.js.
• Add methods to interface with Meteor.Collection
• [~] Arrange a pleasant api of some sort (w/ docs)
• Publish to Atmosphere!
• Create demonstrations & examples
• Refine, improve, and re-design...

API

######Although Evo isn't quite ready for primetime according to my standards, it is perfectly capable of producing evolutionary effects and modeling data.

####Quick-start

1. Navigate to your Meteor project's local directory.

2. Ensure that Meteorite is installed.

3. Add this package via mrt:

   mrt add Evojs

4. Take a look at the example below, and you're ready to go!

####Important Objects

1. Leaf
	- an end node of a genetic tree

2. Node (inherits Leaf)
	- a node with two children, which can either be leaves or trees

3. Tree (inherits Node)
	- inherited Node becomes top node of tree
	- provides primary methods of evolution

4. Population
	- an end node of a genetic tree.

####Example Implementation

Given objects of the same type along with pre-determined ratings, Evo's 'Population' prototype attempts to model multi-variable inputs. Assume our data has two sources of input, 'x' and 'y', and that we're trying to model the classic XOR problem. Thus, we have the input-output table:

{x: 0, y: 0}  -->  0
{x: 0, y: 1}  -->  1
{x: 1, y: 1}  -->  0
{x: 1, y: 0}  -->  1

In order to model this dataset with Evo, we could do the following:

0. Format our data into arrays

   // input array of objects
   inputs = [{x: 0, y: 0}, {x: 0, y: 1}, {x: 1, y: 1}, {x: 1, y: 0}];
   
   // output array of numbers/ratings
   outputs = [0, 1, 0, 1];
   

1. Create a population of genetic trees

   // 100 individuals in our population
   ourPop = new Population[inputs, outputs, 100]; 
   

2. Impose selection upon them according to our data

   // impose 100 generations of evolution
   ourPop.evolve(100);
   

3. Assess & utilize resulting model

   quantify model success

   rmse = ourPop.bestRMSE; // best 
   rmse = ourPop.medRMSE;  // median
   rmse = ourPop.avgRMSE;  // average
   

   grab a function that represents the model

   ourFunc = ourPop.bestTree.toFunc();

   in order to utilize the product:

   ourFunc( {x: 0, y: 0} )  --> 0
   ourFunc( {x: 1, y: 0} )  --> 1
   ourFunc( {x: 0, y: 1} )  --> 1
   ourFunc( {x: 1, y: 1} )  --> 0
   ...
   

4. Save & Load Tree, Population

   generate string for database insertion:

   // for population
   popStr = JSON.stringify(pop);
   
   // for best tree model
   treeStr = JSON.stringify(pop.bestTree);
   

   loading from previously saved string:

   loadedPop = fromJSON.Population(popStr);
   
   loadedTree = fromJSON.Tree(treeStr);