source: branches/OKBJavaConnector/ECJClient/src/ec/app/sum/steadysum.params @ 13402

# 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 = ../../steadystate/steadystate.params
parent.1 = sum.params

# The stuff above is all you need to do single-population
# steady-state evolution.  But there are some additional
# gizmos you should be aware of.

# 0. By default, the "deselector" (the selection operator
# responsible for determining who in the population will
# die) is tournament selection of size 1 -- that is,
# random selection.  Let's say you'd like it to be more
# aggressive, say, tournament selection of size 2, picking
# the worse individual.  You can do that like this:
# 
# steady.deselector.0.size =  2 
#
# Or much more aggressive:
#
# steady.deselector.0.size =  7
#
# You can change the deselector entirely but it needs
# to be of SteadyStateBreedingSourceForm, and the most
# obvous choice (the classic for Steady State GA's,
# BestSelection set to pick-worst) is actually not of
# this form because it's expensive to scan through the
# whole population each time.

# 1. By default, the steady-state evolution runs for some
# number of *generations*, where a generation is defined
# as the time in which N individuals are evaluated, where
# N is the size of subpopulation 0.  The first generation
# is of course special as all the individuals are
# evaluated together.  Subsequent "generations" are just
# however long it takes for N more individuals to be
# evaluated.  You can change the run-length from being
# based on generations to being based on number of
# evaluations if you want something more precise.  To do
# that you'd set:
#
#evaluations = 100 
#
# Which says how many evaluations you want.  Obviously
# this must be larger than the initial population size...

# 2. When you run in its current form, this parameter
# file will cause the system to complain that you didn't
# provide a SteadyStateStatistics object.  This is okay,
# but you may not get the statistics you expect.  For
# more information, see SteadyStateEvolutionState

# statistics =    ...

# 3. You want to specify whether or not to allow any
# children to enter the population if they are identical
# to some existing member of the population.  Disallowing
# duplicates in this way is the traditional approach to
# steady-state evolution.  At present, each time we 
# need to make an individual, we try up to 100 times to
# create one that's not a duplicate.  You can change
# the number of times to retry (or set to 0 to not
# retry) by changing this parameter:

# steady.duplicate-retries =    ...