Module tutorial3

source code

tutorial3

Contains all parameters for the evolutionary run, grammar rules, constraints, and specifics about the terminal and function set of the trees in tutorial3. This example file gather all the settings for a 'multiple' polynomial regression. We want to evolve a system of ordered polynomial equations by using the mathematical operators: '+','*' and the ADF: ADF1 and ADF2, in the said order. an ADF2 branch could then call for an ADF1 terminal, but not the opposite. We try to find the following model being shaped as a system of polynomials:

|ADF1=x+y

|ADF2=ADF1^3

from 30 sets of testing data (30 different values for x and y). This means the each tree has to be evaluated for every different example, so 30 times. There is an alternative solution which we will see in the next tutorial, where instead of evaluating a tree 30 times, we will input an array of 30 data in the variable leafs of the tree. Considering the constraints for building the trees, the root node will have 2 children ADF1 and ADF2 (in order), and ADF2 must be able to reuse ADF1 as a terminal... A typical way to run the tutorial would be to:

• modify the settings file so that:

  >>>     # setup for running tutorial 3
          functions = tutorial3.functions
          crossover_mapping=tutorial3.crossover_mapping
          nb_eval=tutorial3.nb_eval
          ideal_results=tutorial3.GetIdealResultsData()
          terminals =tutorial3.terminals
          Strongly_Typed_Crossover_degree=tutorial3.Strongly_Typed_Crossover_degree
          Substitute_Mutation=tutorial3.Substitute_Mutation
          treeRules = tutorial3.treeRules
          adfOrdered = tutorial3.adfOrdered
          FitnessFunction = tutorial3.FitnessFunction

• run the evolution in the main method, and make sure that the root node parameter only have two children.e.g.

  >>>    import evolver
  if __name__ == "__main__":
  >>>    dbname=r'C:\pop_db'
  >>>    evolver.EvolutionRun(2000,(0,2,'root'),2,8,'AddHalfNode',100, 0.00001 ,0.5,0.49,7,0.8,dbname,True)

  This means that we define:

• a population size of 2000 individuals,
• a root node with 2 children
• a minimum tree depth of 2
• a maximum tree depth of 8
• the use of a Ramped half and Half Koza tree building method
• a maximum number of runs of 100 generations before we stop
• a stoping fitness criteria of 0.1 (if the fitness<=0.1, solution found)
• a crossover probability of 0.5
• a mutation probability of 0.49
• a reproduction probability of 0.01 (automatically deduced from the 2 previous values)
• the size of the tournament selection
• the probability of selecting the fitttest during tournament selection
• a database of name and path dbname
• a run done in verbose mode (printing the fittest element of each generation)

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