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myCGSGP.params
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myCGSGP.params
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# Copyright 2006 by Sean Luke and George Mason University
# Licensed under the Academic Free License version 3.0
# See the file "LICENSE" for more information
parent.0 = ./gp-workspace-2016Competent/Evolution/src/ec/gp/koza/koza.params
jobs = 0
#To set the jobs more than one, you should reset SimpleNodeBase.totalEvaluationCount in the setup function of ec.gp.semantic.EvoState.java
seed.0 = 123456
state = ec.gp.semantic.EvoState
#stat.silent.file = true
stat = ec.gp.semantic.statistics.ExtendedStatistics
stat.printIndividual = true
stat.calculateUniqueSemantics = true
stat.calculateUniqueIndividuals = true
evaluationLimit = 10000000
stat.out = ./gp-workspace-2016Competent/Evolution/out.stat
stat.equivalenceChecker = ec.gp.syntactic.equivalence.IEquivalenceChecker
libraryFactory = ec.gp.semantic.library.DoublePopulationLibraryFactory
pop.subpop.0.species.fitness = ec.gp.koza.KozaFitness
#Breeding parameters
init = ec.gp.GPInitializer
pop.subpop.0.species = ec.gp.GPSpecies
pop.subpop.0.species.ind = ec.gp.GPIndividual
pop.subpop.0.size = 1000
generations = 1
# We retry 100 times for duplicates (this is the lil-gp default)
# in our subpopulation 0
pop.subpop.0.duplicate-retries = 1
# That GPIndividual has a single tree, which uses the
# "tc0" Tree Constraints (which we define below later)
pop.subpop.0.species.ind.numtrees = 1
pop.subpop.0.species.ind.tree.0 = ec.gp.GPTree
pop.subpop.0.species.ind.tree.0.tc = tc0
# The GPSpecies has 2 pipelines, Crossover and Reproduction,
# chosen with 0.9 and 0.1 likelihood respectively.
pop.subpop.0.species.pipe = ec.breed.MultiBreedingPipeline
pop.subpop.0.species.pipe.generate-max = false
pop.subpop.0.species.pipe.num-sources = 2
pop.subpop.0.species.pipe.source.0 = ec.gp.semantic.breed.CX
pop.subpop.0.species.pipe.source.0.prob = 0.5
pop.subpop.0.species.pipe.source.1 = ec.gp.semantic.breed.CM
pop.subpop.0.species.pipe.source.1.prob = 0.5
#
# Here we define the default values for Crossover,
# Reproduction, Mutation, as well as our selection
# approaches (Koza I). These can be overridden on a per-species
# level of course.
#
pop.subpop.0.species.pipe.source.0.source.0 = ec.select.TournamentSelection
pop.subpop.0.species.pipe.source.0.source.1 = same
pop.subpop.0.species.pipe.source.1.source.0 = ec.select.TournamentSelection
gp.breed.CX.ns.0 = ec.gp.semantic.nodeselectors.LinearProbabilityNodeSelector
gp.breed.CX.ns.1 = same
gp.breed.CM.ns.0 = ec.gp.semantic.nodeselectors.LinearProbabilityNodeSelector
gp.breed.CM.build.0 = ec.gp.semantic.initialization.CI
pop.subpop.0.species.pipe.source.1.build.0.detector = ec.gp.semantic.geometry.L2ConvexHullDetector
gp.breed.CX.tries = 1
gp.breed.CX.maxdepth = 17
gp.breed.CM.tries = 1
gp.breed.CM.maxdepth = 17
gp.breed.CX.inverter = ec.gp.semantic.inverters.DoubleSemanticInverter
gp.breed.CM.inverter = ec.gp.semantic.inverters.DoubleSemanticInverter
gp.breed.CX.midpointProvider = ec.gp.semantic.geometry.SimpleDoubleMidpointProvider
# Reproduction will use Tournament Selection
breed.reproduce.source.0 = ec.gp.semantic.select.CTSStat
# the next four items are already defined in koza.params, but we
# put them here to be clear.
# We have one function set, of class GPFunctionSet
gp.fs.size = 1
gp.fs.0 = ec.gp.GPFunctionSet
# We'll call the function set "f0".
gp.fs.0.name = f0
# We have five functions in the function set. They are:
gp.fs.0.size = 9
gp.fs.0.func.0 = ec.app.semanticGP.func.numeric.X0
gp.fs.0.func.0.nc = nc0
#gp.fs.0.func.1 = ec.app.semanticGP.func.numeric.X1
#gp.fs.0.func.1.nc = nc0
gp.fs.0.func.1 = ec.app.semanticGP.func.numeric.Sum
gp.fs.0.func.1.nc = nc2
gp.fs.0.func.2 = ec.app.semanticGP.func.numeric.Sub
gp.fs.0.func.2.nc = nc2
gp.fs.0.func.3 = ec.app.semanticGP.func.numeric.Mul
gp.fs.0.func.3.nc = nc2
gp.fs.0.func.4 = ec.app.semanticGP.func.numeric.Div
gp.fs.0.func.4.nc = nc2
gp.fs.0.func.5 = ec.app.semanticGP.func.numeric.Sin
gp.fs.0.func.5.nc = nc1
gp.fs.0.func.6 = ec.app.semanticGP.func.numeric.Cos
gp.fs.0.func.6.nc = nc1
gp.fs.0.func.7 = ec.app.semanticGP.func.numeric.Exp
gp.fs.0.func.7.nc = nc1
gp.fs.0.func.8 = ec.app.semanticGP.func.numeric.Log
gp.fs.0.func.8.nc = nc1
#gp.tc.size = 1
#gp.tc.0 = ec.gp.GPTreeConstraints
#gp.tc.0.name = tc0
#gp.tc.0.fset = f0
#gp.tc.0.returns = nil
gp.tc.0.init = ec.gp.semantic.initialization.CI
#gp.tc.0.init.min-size = 2
gp.tc.0.init.max-depth = 6
gp.tc.0.init.detector = ec.gp.semantic.geometry.L2ConvexHullDetector
#eval.problem = ec.app.regression.Nguyen07
eval.problem = ec.app.semanticGP.Regression
eval.problem.data = ec.app.semanticGP.RegressionData
#eval.problem.data = ec.app.regression.RegressionData
eval.problem.size = 180
testCases.factory = ec.app.regression.testcases.Nguyen07Factory
testCases.from = 0
testCases.to = 2
testCases.count = 20
#print-params = true
#print-accessed-params = true
#print-unaccessed-params = true
stat.file = $out.stat
quit-on-run-complete = false