1.3.1 Controller

1.3.2 Evolver

1.3.3 Expressor

1.3.4 Evaluator

1.3.5 Population

1.3.6 Critter

1.4.1 Requirements

1.5.1 Note Well

1. RubyNEAT
   
   • GitHUB RubyNEAT GitHub

   • Ruby GEM

     gem install rubyneat --pre
     

2. RubyNEAT Examples
   
   • Github Example Neaters on GitHub

1.6.1 The XOR Neater Example

require 'xor'
include NEAT::DSL

def xor(*inp)
  inp.map{|n| (n > 0) ? 1 : 0}.reduce {|p, i| p + ((i > 0) ? 1 : 0) } == 1
end

XOR_INPUTS = 2
XOR_STATES = 2 ** XOR_INPUTS
MAX_FIT    = XOR_STATES
ALMOST_FIT = XOR_STATES - 0.5

define "XOR System" do

inputs {
  cinv = Hash[(1..XOR_INPUTS).map{|i| [("i%s" % i).to_sym, InputNeuron]}]
  cinv[:bias] = BiasNeuron
  cinv
}

outputs out: TanhNeuron

hidden tan: TanhNeuron

1. Settings
   

   For RubyNEAT. Extensive documentation will be provided on a later date as to the meanings, which closely follow the parameters for Ken Stanley's NEAT implementation.

   1. General
      

      hash_on_fitness false
      start_population_size 30
      population_size 30
      max_generations 10000
      max_population_history 10
      

   2. Evolver probabilities and SDs
      

      Perturbations

      mutate_perturb_gene_weights_prob 0.10
      mutate_perturb_gene_weights_sd 0.25
      

   3. Complete Change of weight
      

      mutate_change_gene_weights_prob 0.10
      mutate_change_gene_weights_sd 1.00
      

   4. Adding new neurons and genes
      

      mutate_add_neuron_prob 0.05
      mutate_add_gene_prob 0.20
      

   5. Switching genes on and off
      

      mutate_gene_disable_prob 0.01
      mutate_gene_reenable_prob 0.01
      
      interspecies_mate_rate 0.03
      mate_only_prob 0.10 *0.7
      

   6. Mating
      

      survival_threshold 0.20 # top % allowed to mate in a species.
      survival_mininum_per_species  4 # for small populations, we need SOMETHING to go on.
      

   7. Fitness costs
      

      fitness_cost_per_neuron 0.00001
      fitness_cost_per_gene   0.00001
      

   8. Speciation
      

      compatibility_threshold 2.5
      disjoint_coefficient 0.6
      excess_coefficient 0.6
      weight_coefficient 0.2
      max_species 20
      dropoff_age 15
      smallest_species 5
      

   9. Sequencing
      

      The evaluation function is called repeatedly, and each iteration is given a monotonically increasing integer which represents the sequence number. The results of each run is returned, and those results are evaluated elsewhere in the Neater.

      start_sequence_at 0
      end_sequence_at 2 ** XOR_INPUTS - 1
      

2. The Evolution Block
   

   evolve do
   

   1. The Query Block
      

      This query shall return a vector result that will serve as the inputs to the critter.

      query { |seq|
        * We'll use the seq to create the xor sequences via
        * the least signficant bits.
        condition_boolean_vector (0 ... XOR_INPUTS).map{|i| (seq & (1 << i)) != 0}
      }
      

   2. The Compare Block
      

      Compare the fitness of two critters. We may choose a different ordering here.

      compare {|f1, f2| f2 <=> f1 }
      

   3. The Cost of Fitness Block
      

      Here we integrate the cost with the fitness.

      cost { |fitvec, cost|
        fit = XOR_STATES - fitvec.reduce {|a,r| a+r} - cost
        $log.debug ">>>>>>> fitvec *{fitvec} => *{fit}, cost *{cost}"
        fit
      }
      

   4. The Fitness Block
      

      The fitness block is called for each activation and is given the input vector, the output vector, and the sequence number given to the query. The results are evaluated and a fitness scalar is returned.

      #+BEGIN_SRC ruby fitness { |vin, vout, seq| unless vout * :error bin = uncondition_{booleanvector} vin bout = uncondition_{booleanvector} vout bactual = [xor(*vin)] vactual = condition_{booleanvector} bactual fit = (bout * bactual) ? 0.00 : 1.00 simple_fitnesserror(vout, vactual.map{|f| f * 0.50 }) bfit = (bout ** bactual) ? 'T' : 'F' fit else $log.debug "Error on *{vin} [*{seq}]" 1.0 end } #+ END_SRC

   5. The Termination Condition
      

      When the desired fitness level is reached, you may want to end the Neater run. If so, provide a block to do just that.

        stop_on_fitness { |fitness, c|
          puts "*** Generation Run *{c.generation_num}, best is *{fitness[:best]} ***\n\n"
          fitness[:best] >= ALMOST_FIT
        }
      end
      

3. Report Generating Block
   

   This particular report block just adds something to the log. You could easily replace that with a visual update if you like, etc.

   report do |rept|
     $log.info "REPORT *{rept.to_yaml}"
   end
   

4. Engine Run Block
   

   The block here is called upon the completion of each generation. The 'c' parameter is the RubyNEAT Controller, the same as given to the stop_onfitness block.

   run_engine do |c|
     $log.info "******** Run of generation %s completed, history count %d ********" %
           [c.generation_num, c.population_history.size]
   end
   

1.6.2 Releases

1. v0.4.0.alpha.4
   
   • First crude cut of a dashboard rubyneat_dashboard
2. 0.3.5.alpha.6
   
   • Command line workflow is a bit cleaner
   • Removed neater examples completely and place them in https://github.com/flajann2/rubyneat_examples
   • Cleaned up the internal docs a bit
   • Uniquely Generated Named Objects (UGNOs) cleaned up to be respectable
3. 2015-06-08
   
   • Working on the Iterated ES HyperNEAT still, after being side-tracked by having to make a living. Also creating a maze environment for the critters to operate as bots in order to test the new ES HyperNEAT extension.
   • rnDSL, as a result of TWEANN Compositions, is undergoing radical changes. All example Neaters will be eventually update to reflect the new syntax.
4. 2014-09-25
   

   Hot on the efforts on adding two major features to RubyNEAT:

   • TWEANN Compositions – you will be able to define composites of TWEANNs on a per critter basis. This should mirror how, say, biological brains composite themselves into regions of speciality. You may specify different selections of neurons for each TWEANN. This is totally experiential, so we'll see if this results in better convergence for some problems.
   • iterated ES HyperNEAT – one of the compsitions above can be specified as a Hyper TWEANN, and just represent one of the many compositions you may have.
   • The syntax of the Neater DSL will change quite a bit to reflect the new features, and all of the examples will be rewritten to show this.

   Do not confuse the ANN compositions here with CPPNs, which are completely different. By default, all TWEANNs in HyperNEAT are potential CPPNs anyway, as you can specify more than one neuron type.

5. 2014-08-03
   

   Just released a very crude alpha cut of a dashboard for RubyNEAT. You will have to install it manually, along with rubyneat. The gem is rubyneat_dashboard.

   • I am currently working on a Dashboard for RubyNEAT. It will be a gemmable plugin that will allow you to use the browser as the dashboard. It will have realtime updates and the like, allowing you to monitor the progress of your Neaters, and to view and possibly set parameters, and to see what your Critters look like.