As already said, the behavior of the algorithms
will be exactly the same as the previous one as far as optimization is
concerned. Only now you will be able to tune every component of the algorithms
(except the type of genotype) using run-time parameters.
Also, as in previous lessons, most of the code
is representation-independent, i.e. is the same for both the binary genotypes
and the real-valued genotypes. This small user's guide reflects that, but
you can go directly to the binary or the real
parts if you wish.
Warning: this is a user guide, not a programming guide. In particular, the keywords of the parameters are not the names of the underlying classes (though they should be similar in most cases).
User's guide: Parameter input The way to input parameters has already be described in Lesson 3. To get a list of parameters, type the command with option --help (or -h): with both testBit and testReal this will result in
User's guide:Representation-independent
parameters
The parameters are organized in sections. In
what follows, the fixed font colored text is directly taken from the status
file and is commented between the lines.
# --seed=988700289 # -S :
Random number seed
Unsigned long parameter:
the seed for the Random Number Generator
If the parameter is absent, then time(0) is used, which indicates the number
of seconds since Jan. 1 1980, is used ... and stored in the status file,
of course, so you can repeat the same run by simply assigning that value
again. There is no default value ("true" random
seed).
# --useEval=1 # Use nb of
eval. as counter (vs nb of gen.)
Boolean parameter:
whether or not you want the nb of evluations to be displayed and used as
counter in statistics outputs and plots. Default
is true.
# --printBestStat=1 # Print
Best/avg/stdev every gen.
Boolean parameter:
toggles screen output of indicated statistics. Default
is true.
# --plotBestStat=0 # Plot
Best/avg Stat
Boolean parameter:
toggles gnuplot output of best and average plots (Linux only at the moment).
Default is false.
# --BestFileName=best.xg
# Name of file for Best/avg/stdev
String parameter:
if present, the statistics are stored in that file (no
default)
# --printPop=0 # Print sorted
pop. every gen.
Boolean parameter:
adds a dump of the whole population to the screen every generation. Is
likely to generate huge
output! Default is false.
# --printFDC=1 # Print FDC
coeff. every gen.
Boolean parameter:
adds Fitness Distance Correlation to output every generation. Default
is false.
# --plotFDCStat=0 # Plot
FDC scatter plot
Boolean parameter:
toggles the Fitness Distance Correlation plot (Fitness vs distance to best).
Default is false.
# --plotHisto=0 # Plot histogram
of fitnesses
Boolean parameter:
if on, gnuplot is used to plot the sorted population (fitness vs rank).
Gives a graphical idea of the diversity. Default
is false.
# --Load= # -L : A save file
to restart from
String parameter:
if present, the initial population (and the RNG) is read from indicated
file. That file must
come from a previous save (or must be in same format!), i.e. must contain
a popualtion, the RNG and all parameters. If no other parameter is modified,
using a previously saved population and RNG will give exactly the same
results than having run that previous run longer. And a way to be sure
to re-use the same parameters is to ... use that very save file as parameter
file, as it contains all actual parameters in the right format.
Note that if not enough individuals are read,
the remaining are randomly initialized. No
default value.
# --recomputeFitness=0 #
-r : Recompute the fitness after re-loading the pop.?
Boolean parameter:
in case some individuals are read from a file, their fitness is read too.
If this one is true, it is nevertheless recomputed. Default
is false i.e. use fitnes that's in the file.
# --saveFrequency=0 # Save
every F generation (0 = only final state, absent = never)
Integer parameter:
interval between two dump to disk of the whole population (+RNG + parameters)
to disk, in a file named genNN.sav, where NN is the generation number.
If this prameter is present (even with 0 or negative value), the final
population will always be saved, whatever the reason for stopping. Hence
the only way to avoid all saves is to omit the parameter (there is no
default value).
# --saveTimeInterval=0 #
Save every T seconds (0 or absent = never)
Integer parameter:
time interval between two population (+RNG + parameters) dumps to disks.
Files are names timeNN.sav. See pervious parameter description for ore
details. No default value.
# --status=t-eoGA.status
# Status file
String parameter:
name of the status file (that contains all parameters in the input format).
There is no way to avoid creating that file except recompiling ... or giving
the name /dev/null (Unix). Default value is ProgramName.status
# --maxGen=100 # -G : Maximum
number of generations (0 = none)
Integer parameter: maximum number of generations.
A value of 0 disables that stopping criterion. Default
is 100.
# --steadyGen=100 # -s :
Number of generations with no improvement
Integer parameter:
stops whenever that number of generations is passed without any improvement
of the best fitness in the population, provided the following minimum number
of generations has been done. No default value.
# --minGen=0 # -g : Minimum
number of generations
Integer parameter: the above steadyGen parameter
starts its job only after that minimum nuber of generations is passed.
No default value.
# --maxEval=0 # -E : Maximum
number of evaluations (0 = none)
Integer parameter: maximum number of generations.
No default value.
# --targetFitness=0 # -T
: Stop when fitness reaches
Real-valued parameter: the algorithm stops whenever
the best fitness reaches that target. No default
value.
# --CtrlC=0 # -C : Terminate
current generation upon Ctrl C
Boolean parameter: if true, Ctrl C only stops
after the current generation as completed (eventually dumping population
to a file if some saver is active). THis very useful feature is only available
in Unix at the moment. Default is false.
# --selection=DetTour(2)
# -S : Selection: Roulette, DetTour(T), StochTour(t) or Sequential(ordered/unordered)
String parameter:
Name of selection procedure. Availabable
are the roulette wheel
(name Roulette,
fitness scaling coming soon); deterministic
tournament (name DetTour
with size - integer > 2 - in parentheses right after the name, use double
quotes on the command line); stochastic
tournament (name StochTour
with probability - float in [0.5, 1] - in parentheses); sequential
(name
Sequential,
all individuals in turn), either from best to worst (option ordered
in parentheses), or in random ordered (option unordered);
and finally repeated independent uniform
choices (name Random).
Default is DetTour(2).
# --nbOffspring=100% # -O
: Nb of offspring (percentage or absolute)
Integer or real-valued parameter:
this parameter indicates the amount of
offspring that will be generated from
the genitors every generation. However, this amount can be specified either
relative to the
population size, and it should then end with percent character (%), or
as an absolute
integer number (without the percent char).
Indeed, you can either want, say 7 times more
offspring than parents (a rather common situation in Evolution Strategies),
in which case you give value 700% to nbOffspring
parameter; or you might want a single offspring whatever the population
size, like in Steady-State evolution engines, in which case you simply
enter value 1. Default is 100%.
# --replacement=Comma # -R
: Replacement: Comma, Plus, EPTour(T), SSGAWorst, SSGADet(T), SSGAStoch(t)
String parameter:
Name of replacement procedure. Availabable are the ES
plus and comma deterministic replacement
strategies (named respectively Plus
and Comma);
EP stochastic tournament
(name EPTour
with tournament size in parentheses); and the steady-state
replacement procedures, at the moment
only based on fitnesses, replacement being either deterministic
(new born replace worst parent, name SSGAWorst),
or based on a tournament (name SSGADet
for deterministic tournament, size in parentheses, and SSGAStoch
for stochastic tournament, probability in parentheses). Default
is Comma (which btw is also SGA generational
replacement whenever there are as many offspring
as parents).
# --weakElitism=0 # -w :
Old best parent replaces new worst offspring *if necessary*
Boolean parameter:
if true, weak elitism is added to the replacement procedure (i.e. if the
best fitness among the offspring is less than the best fitness, the best
parent replaces the worst offspring). Default
is false.
User's guide:
Bistring specific parameters