Genetic algorithm for node.js
CoffeeScript JavaScript
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Genetics

Genetics is a genetic algorithm implementation for node;
Genetics is written in CoffeeScript (it can be used in JavaScript as well).

Installation

npm install Genetics

Usage

See test/test.coffee for a quick start.

Class Genetic(solType, totalPop = 100, keepPop = 0, crossover = 0.5)

solType must be an implementation of class Solution() totalPop is the total number of solutions keepPop is the number of solutions kept after selection (by default : totalPop / 2) crossover is the crossover rate. The number of solutions generated by cross-over will be (totalPop - keepPop) * crossover. This class must implement the folowing method : after implementation, Genetic.init() and Genetic.run() must be called

Genetic.end()

return true if end Conditions is reached else return false

Genetic.select() (optional)

Select Solutions to be kept from @pop After the Selection, @pop.length must equal @totalPop

Class Solution()

This class must implement the following methods :

Solution.random()

Generate a random solution.

Solution.crossOver(sol1, sol2)

Generate a solution from sol1 and sol2

Solution.mutate()

Return a mutated solution

Solution.eval()

Set Solution.fit to a number representing the solution's fitness

Documentation

Genetics.pop<Array>

contain all solutions

Genetics.gen<Number>

current generation

Genetics.constructor(@solType, @totalPop = 100, @keepPop = 0)

  • set @solType, @totalPop and @keepPop
  • if @keepPop == 0, then @keepPop = @totalPop / 2
  • create an empty array @pop

Genetics.init()

  • generate @totalPop solutions calling Solution.random()
  • solutions are pushed into @pop

Genetics.sort()

  • sort @pop by fitness
  • solutions must have been evaluated before calling @sort()

Genetics.bestfit()

  • return bestfit after calling @sort()

Genetics.eval()

  • call Solution.eval() for each solution in @pop

Genetics.random()

  • return a randomly chose solution from @pop

Genetics.run()

  • run genetic algorithm
  • initial pop is eval
  • initialize @gen to 0
    • @end() is checked
    • newPop is generated with Solution.crossOver() and Solution.mutate()
    • newPop is eval by calling Solution.eval() for each new solution
    • newPop is added to @pop
    • if @select() is not implemented, the @totalPop best Solution are selected
    • else @select() is called, an exception is threw if @pop.length != @totalPop
    • @gen is incremented

Genetics.totalFit()

  • return total Fitness of @pop

Genetics.rws(f = null)

  • return a random solution selected by roulette wheel
  • f is a random number, auto-generated if f == null

Genetics.selectRWS()

  • select by roulette wheel sampling
  • using this selection method, Solution.eval must return a value in [0,1]
  • implementation may looks like : select: -> @selectRWS()

Genetics.selectRWS2() (cheated :p)

  • select by roulette wheel sampling, with higher probability for bests fit
  • using this selection method, Solution.eval must return a value in [0,1]
  • implementation may looks like : select: -> @selectRWS2()

Genetics.selectSUS()

  • select by Stochastic universal sampling
  • using this selection method, Solution.eval must return a value in [0,1]
  • implementation may looks like : select: -> @selectSUS()