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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

# This file is used only by the slaves, not the master.  The shared
# parameters between the master and slaves should instead be defined
# in the master.params file.

parent.0 = master.params



# Specifies the IP address of the master, for the Slaves
# to connect.  Slaves need to be told this in their
# parameter files.  The example below sets it to localhost,
# which is probably not what you want.
eval.master.host = 127.0.0.1


# Specifies a slave's name for debugging purposes.  If this is not
# set (the default), then the system will use an approximately unique
# name constructed from the IP address and startup time of the
# slave.  Names don't have to be unique -- it's only for debugging.

# eval.slave.name = my-slave-name


# Specifies the mode the slave runs in.  If false, the slave runs in
# 'regular' mode where it just evaluates the individuals and returns
# either the fitnesses or the whole individuals.  If true, the slave
# runs in 'evolve' mode, where it evaluates the individuals, but then
# if there's extra time (see next parameter), it will treat the individuals
# as its own mini-population and do some evolution on that population
# according to the evolution parameters you provided the slave.  When
# the time is up, it then returns the entire individuals.
#
# You want to make sure that if you set this to true, then the mini-population
# on the slave is sufficiently large to support its own evolution.
# The population size is specified by eval.master-problem.job-size
# on the Master.
eval.slave.run-evolve = false


# Specifies the length of (wall clock) time, in milliseconds, that the
# slave should do "evolution" on its individuals if eval.run-evolve = true
eval.slave.runtime = 6000


# Change this to force whole Individuals to be returned by the
# Slave rather than just returning Fitnesses, plus whether or not the
# Individual was evaluated.  Returning a whole Individual is expensive
# over the network and should only be done if, for some reason, your
# Problem class modified the Individuals as it evaluates them.
eval.return-inds = false


# Set this to FALSE (it's true by default) to cause the slave to linger
# after the Master has terminated.  The slave will then wait for another
# Master to start up on that port and host and will then connect to it.
# Essentially we're creating a slave daemon.

# eval.slave.one-shot = false


# Set this to TRUE (it's false by default) to cause the slave to
# muzzle its stdout and stderr logs, effectively eliminating all
# printing to the screen.

# eval.slave.muzzle = true