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ECJ, A Java-based Evolutionary Computation Research System. ECJ is a research EC system written in Java. It was designed to be highly flexible, with nearly all classes (and all of their settings) dynamically determined at runtime by a user-provided parameter file. All structures in the system are arranged to be easily modifiable. Even so, the system was designed with an eye toward efficiency. ECJ is developed at George Mason University's ECLab Evolutionary Computation Laboratory. The software has nothing to do with its initials' namesake, Evolutionary Computation Journal. ECJ's sister project is MASON, a multi-agent simulation system which dovetails with ECJ nicely.

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# Copyright 2010 by Sean Luke and George Mason University
# Licensed under the Academic Free License version 3.0
# See the file "LICENSE" for more information

# We don't include koza.params as a parent here -- doing so creates
# lots of parameter file inheritance problems.  Instead you should
# include this parameter file FIRST, and then include something 
# which ultimately includes the koza.params parameter file later.


# The problem is redefined as a GEProblem.  The ACTUAL problem
# will be defined in eval.problem.problem

eval.problem = ec.gp.ge.GEProblem

pop.subpop.0.duplicate-retries = 100

pop.subpop.0.species = ec.gp.ge.GESpecies
pop.subpop.0.species.parser = ec.gp.ge.GrammarParser

# The individual is a GEIndividual
pop.subpop.0.species = ec.gp.ge.GESpecies
pop.subpop.0.species.ind = ec.gp.ge.GEIndividual
pop.subpop.0.species.fitness = ec.gp.koza.KozaFitness

# We'll allow 256 possibilities per gene (the maximum anyway
# since GEIndividual is a ByteVectorIndividual).  Crossover-type
# is entirely unused.
pop.subpop.0.species.min-gene = -128
pop.subpop.0.species.max-gene = 127
pop.subpop.0.species.mutation-prob = 0.01
pop.subpop.0.species.crossover-type = one

# Individuals are built using geometric series.  The minimum allowed size is 5. 
pop.subpop.0.species.genome-size = uniform
pop.subpop.0.species.min-initial-size = 15
pop.subpop.0.species.max-initial-size = 25

# The following pipeline is relatively common in GE.
# The BufferedBreedingPipeline forces two individuals at a time
# in the steady state case (see the manual on steady state evolution),
# but otherwise is relatively harmless for generational unless for
# some insane reason you have a population with an odd numbered size.

pop.subpop.0.species.pipe = ec.breed.BufferedBreedingPipeline
pop.subpop.0.species.pipe.num-inds = 1
pop.subpop.0.species.pipe.likelihood = 1.0
pop.subpop.0.species.pipe.source.0 = ec.vector.breed.VectorMutationPipeline
pop.subpop.0.species.pipe.source.0.likelihood = 1.0
pop.subpop.0.species.pipe.source.0.source.0 = ec.gp.ge.breed.GECrossoverPipeline
pop.subpop.0.species.pipe.source.0.source.0.likelihood = 0.9
pop.subpop.0.species.pipe.source.0.source.0.source.0 = ec.select.TournamentSelection
pop.subpop.0.species.pipe.source.0.source.0.source.1 = same
select.tournament.size = 7

# By default we don't allow wrapping
ge.species.passes = 1
# init-scheme
ge.species.init-scheme = default


# Build the dummy GP Individual information.  This stuff is necessary to convince
# GP that all is well and good with the trees that GE is creating and handing to
# GP to evaluate.

pop.subpop.0.species.gp-species = ec.gp.GPSpecies
pop.subpop.0.species.gp-species.fitness = ec.gp.koza.KozaFitness
pop.subpop.0.species.gp-species.ind = ec.gp.GPIndividual
pop.subpop.0.species.gp-species.ind.numtrees = 1
pop.subpop.0.species.gp-species.ind.tree.0 = ec.gp.GPTree
pop.subpop.0.species.gp-species.ind.tree.0.tc = tc0
# We'll never use this, so let's set it to Reproduction, which is simple
pop.subpop.0.species.gp-species.pipe = ec.breed.ReproductionPipeline
pop.subpop.0.species.gp-species.pipe.num-sources = 1
pop.subpop.0.species.gp-species.pipe.source.0 = ec.select.TournamentSelection

# stat = ec.simple.SimpleShortStatistics