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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 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 = ../ec.params

# 
# The following parameter file will set up a very basic form
# of evolution, single-threaded, no coevolution, no exchanging,
# no cross-population breeding, using generational evolution,
# simple fitness, popsize=1024, etc. 
# You'll need to fill in some of the gaps.
#

# ec.Evolve
# ==============================

# simple generational evolution
state = 				ec.simple.SimpleEvolutionState



# ec.EvolutionState
# ==============================

# We're using the standard initialization method
init = 					ec.simple.SimpleInitializer

# We're using the standard (empty) finalization method
finish = 	        		ec.simple.SimpleFinisher

# We're using the standard (empty) exchange method
exch = 					ec.simple.SimpleExchanger

# We're using standard breeding -- no cross-population breeding
breed =					ec.simple.SimpleBreeder

# We're using standard evaluation -- no coevolution
eval =					ec.simple.SimpleEvaluator

# We're using simple statistics
stat =					ec.simple.SimpleStatistics

# run for 51 generations, quit prematurely if I find something ideal
generations =				51
quit-on-run-complete =			true



# ec.Initializer
# ==============================

pop = 					ec.Population


# ec.Population
# ==============================

# No multiple populations
pop.subpops =				1
pop.subpop.0 = 				ec.Subpopulation


# ec.Subpopulation
# ==============================

# subpop size is 1024 individuals
pop.subpop.0.size =			1024
# don't bother trying to eliminate duplicates from the
# initial population
pop.subpop.0.duplicate-retries =	0

# ==You need to provide the species info for the subpopulation.==

# ec.simple.SimpleBreeder
# ==============================

# By default elitism isn't done.  If you want to do elitism for, say,
# the top 10 individuals in subpopulation 0, you'd say:

# breed.elite.0 = 			10


# ec.SimpleStatistics
# ==============================

# output statistics to the file "out.stat" in the directory
# the run was started in
stat.file				$out.stat