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ControlAction.allele to this Locus.
Countable.getCount().
JButton and:
add it to this object's Container
set its enabled state (from the given parameter)
add this as an action listener to it
add it to the list of buttons
ticks ticks of the clock.
EcoFactor to an
Environment.
gene to this Genotype at its end.
Genotype_.addGene(Gene) for each of the genes provided.
Function_.evaluator
TEST
Phenotype.
trait to this Phenotype.
Genes which "compete" for presence at a LocusAllele,
which are specific, competing genes which may appear at a Locus.Allele_Dominance, which uses upper and lower case to denote
dominance, the two different alleles of the Allele_Sex type are "Y"
and "X" for male and female, respectively.Attraction.Attribute.Visualizable community.Avatar.color.
point and with color black.
point and with given color.
Avatar objects.Identifiable objects.id, and
get the best value for the given candidates, using the
processor if not-null.
key -> value, for example when some
significant amount of calculation is required to generate value and we
therefore prefer to look it up to see if it's already been calculated.Cache.t * sT of a continuous
distribution with mean: f * sF and standard deviation: the
value of Fitness_.bandwidth(String).
Mortality_.calculateViability(int, double, double), given the age,
fitness and saturation.
Census_Sink
TODO do we really need this class? No it is obsolete!SinkCensus specifically which tries to do sensible
things for general types of phenotypes and genotypes.SinkCensus interface.Census_SinkSinkCensus specifically for the PepperedMoth
example and which concentrates on showing the mean wing color (and numbers of
organisms with each trait).OrganismCensusContext, then we call
Organism_.doCensusInContext(OrganismCensusContext) and consider ourselves
done (no need for going deeper through children); 2) otherwise, we output
information about this Organism and return true (OK not yet done
-- need to iterate through children).
female, from among
all of the organisms at the lek.
female, from among
all of the organisms at the lek.
Chromosome
interface.Population addresses the more basic aspects
of an Evolvable, Visualizable and Countable group.epsilon.
epsilon.
epsilon.
epsilon.
ControlPanel_.Mate object based on this (male) organism and its
desirability to the given female.
Mate object based on the given
male Organism and his desirability.
Mate object based on this Organism [provided
that this is male] and the given female.
MateChoice.pairUp(Organism, Lek) for the
female, lek and the appropriate genomic.
MateChoice.pairUp(Organism, Lek) for the
female, lek and the appropriate genomic.
Theological.cullMembers(), passing value
of organisms.
EcoFactor.EcoFactor represents an environmental
factor with a double value.EcoFactor_Number and which is specific to the
soot density of the peppered moth example.EcoFactor
objects.Environment
interface.Environment interface
for the peppered moth example.Environment which is Susceptible to
extended-phenotype-invoked changes.Runnable event which changes the value of an eco
factor in an environment.EnvironmentListener.Genotype_.getBases().
FitnessCacheKey._engine directly but for the
FitnessCacheKey._traits, get the signature and check its equality.
CacheSignature.getSignature() instead of just
_genes.
ExPhen object.
Evolution_ by adding the
properties start, rate for continuously evolving processes [the
Evolution_ class can be run in continuous evolution mode too but it
doesn't really know about rate and start.JApplet understands how to interact with an
Evolution, by virtue of implementing GenerationListener and
ControlAction.EcoFactor which has been modified by an organism whose Genome
is expressed as the ExPhen.ExPhen, named in honor of the nature's
engineer, the Beaver (genus Castor) which creates some of the largest
extended phenotypes found in the animal kingdom, that is to say the lakes
formed by Beaver dams (we humans create even bigger ones, like the Great Wall
of China).ExPhen objects.Trait derived from invoking
Expresser.express(Gene...).
this
Expresser.
Genomic_.express(Phenotype, Colony, Genes) with null population.
Trait or an
ExPhen.
Expresser.
Expresser.
Expresser.EcoFactor_TravelTimes.FACTOR_TRAVELTIMES.
Fecundity interface.other genome into a
new diploid genome.
other genome into a
new diploid genome.
Population_Managed.processBestFit(ProcessBest) with
an appropriate processor.
lek or the alternativeLek
.
lek or the
alternativeLek .
Environment.update(String, Object).
Fitness well suited to
configuration via XML and dependency injection.FitnessEngine interface.FitnessEngine interface.ControlAction.Cache_._cache is already clear.
Evaluator.Choosy.getMinimumDesirability(double)
function and allows dynamic varying of the specific formula, by emplying an
Evaluator.Desirable which extends Function_
and implements HasExpressions (thus it can be updated graphically at
runtime).Evolution runs.Organism belonging to
a Taxon.Genome, the genetic information contained in an Organism belonging to
a Taxon.Genomic interface.Genome, the genetic information contained in an Organism belonging to
a Taxon."ploidy".
id.
Genomic_Sexual object.
Expresser.isComplementary(Locus, String, String) will be true.
Phenome.setData(Object).
d * g * h
where d is the value of the desirability index defined by the superclass
(see MateChoice_.getDesirabilityIndex(Organism, Organism) ; and g
is one more than the age of the candidate male;
TODO consider a different mechanism here: instead of passing in a random
number to the desirability formula, we should divide the random space
into several bands (or tranches), using Randomizer, and then
cache the various desirability values for age/viability/tranche.
name.
Function_.getEvaluator() returns an EvalExpression, delegate to
it.
Function_.getEvaluator() returns an EvalExpression, delegate to
it.
name.
value (usually from one of the
traits of a phenotype), the target (usually a value from an
eco factor in the environment), and the shape factor
(usually determined by the application).
Pharacter_._variants contains an element whose key is the variant's id,
then return the key else null.
Allele_.locus.
Painter for this system.
ExPhen object which
will be suitable to substitute in the environment as the outcome of
processing this extended phenotype.
Function_.evaluator
Valuable objects, for example in conjunction with Best
interface.
FitnessCacheKey._engine directly but for the
FitnessCacheKey._traits, get the signature and use its hash code.
CacheSignature.getSignature() instead of
just _genes.
Visualizable
community, an Organism, for example.VisualizingEvolutionaryApplet.Taxon_.isFinished().
BoxLayout.
Locus.Locus.
Environment_TS and
logs its details.
Avatar, based on an organism.
EcoFactor_Clients.FACTOR_CLIENTS.
Gene_Diploid with the given allele indexes
TraitFactory.makeVariable(Pharacter, double).
Allele_Dominance with the given allele indexes
TEST
ExPhen_Castor
.
Genomic and with genes where the alleles have been picked for
each locus according to the Locus.pickAllele() method.
Genomic and is diploid or haploid according to the
ploidy parameter.
TraitFactory.makeDiscrete(Pharacter, String).
GenomeFactory.makeGamete(com.rubecula.darwin.domain.helper.Genomic) and then fertilizing them.
Population where after
stabilizing on a best (fittest) solution, we update the environment, kill off
the colonies other than the one containing the best organism, create daughter
colonies, each of which has a differently modified environment based on the
original environment.Mating.Meiosis.Meiosis
Meiosis with
given random number source and no crossover.
Meiosis with
given random number source.
Meiosis with
given random number source.
Mortality interface.random for random numbers and
invoking the constructor
Mortality_Reaper.Mortality_Reaper(RandomGenerator, double).
random for random numbers and
invoking the constructor
Mortality_Reaper.Mortality_Reaper(RandomGenerator, Number, Number).
Mutator_.mutate(Allele) method.
Mutator interface.EvolutionTask and run it.
EvolutionTask and run it.
Evolvable
object.
Organism interface.
Organisms in the population by
normalizing its genome in reference to the "best" genome.
FrequencyMap where the keys each have an intrinsic value,
yielded by invoking Number.doubleValue().source has changed and may need an update to the visual
representaion.
p
and, for each one, and, if it is not viable [either just born or newly
dead], either removes the corresponding individual, or (providing that
the age is 0) adds a newly created individual.
EnvironmentListener_Writer._writer.
p has changed.
Evolvable implementer
completes a new generation or when the Evolution itself is
completely exhausted (has no more evolvables to work with).
Best object is found.
OptionsPanel_.OptionsPanel_ for the Peppered Moth.Organism interface.VisualizationModel instance.
Painter.Painter
which.
Mating object based on the female given and a
male chosen from the lek.
Evolution.resume() is called.
Pharacter (a Phenotypic Character).Phenome interface.Phenome interface.Phenotype.PhenotypeFactory.makeDawkinsian().
Population interface.Population.ExPhen.applyToEnvironment(Object).
Best interface.ExPhen.applyToEnvironment(Object), passing in the
criterion.
ExPhen.applyToEnvironment(Object), passing in the
criterion.
Locus.Mating object produce a gamete.
JSlider sets up a slider which controls a property
of the application.JSlider sets up a slider which controls a property
of the application.Expresser for a specific
Locus.
FrequencyMap where the keys each have an intrinsic value,
yielded by invoking Quantifiable.doubleValue().JDKRandomGenerator class as its random number generator.seed.
Collection as an Iterator such that the order of
visiting the elements in the collection is random.Realm.Realm.Registry and, as the
name suggests, it does nothing at all.Chromosome_Sex.
Chromosome_Sex.
VisualizationModel_._avatars map that avatar which is indexed by the
individual referenced by the parameter avatar.
Function_.evaluator
TEST
Best object as if it had been newly constructed.
RandomIterable._list based
on new values from the random number source, i.e.
Evolution.pause().
Client objects.EvolutionTask.isPaused(), then invoke EvolutionTask.tick().
Theological.seedMembers() on each one.
Taxon system
to which we belong by invoking Taxon.getSeedPopulation() and
recording the result as number.
Theological.seedMembers(), passing value
of organisms.
Evolvable which a certain number of
members.
BeanPot#setConfiguration(Class, String, boolean) with the
two parameters given.
BeanPot.setConfiguration(String) with the one parameter
given.
Object.clone().
Taxon_.addPopulation(Population) method.
name and
value.
Timed object.
state.
Allele.
EvolutionaryApplet.init(), after invoking the
super-method.
Attribute_.value.
Number.doubleValue()
Number.doubleValue()
e^(random*3.8)
TEST
Evolvable object
might not undergo a new generation for every "tick" of this evolutionary
clock.]
The result of calling
ScheduledExecutorService.scheduleAtFixedRate(Runnable, long, long, TimeUnit)
results in a new thread being started with the name formed from
EvolutionThreadFactory#namePrefix, space and a sequence number.
ControlAction.setProperty(String, Object) method for the provided
implementation of ControlAction.
ControlAction.setProperty(String, Object) method for the provided
implementation of ControlAction.
thinFactor.
EvolutionTask's clock.
Trait is the phenotypic analog to a (genotypic) Gene.Trait.Trait which is based on a variable value.Trait.getKey() to determine how to uniquely
identify the trait for purposes of the frequency map.Trait instances.Environment_.fireEnvironmentChanged().
Environment.fireEnvironmentChanged().
Best_.updateInternal(ComparableValue, boolean) for each candidate.
Population_Managed and is called by the
Population_Managed.ProcessBestInEnvironment.onUpdate(Organism), which in turn is
invoked when there is a new best organism by
Best_Organism#update(Organism, boolean).
Variant interface.Variant.Visualizable, such as a Population.Visualization_PepperedMoth
VisualizableListener which updates a visualization
model.VisualizationModel.VisualizationModel_._avatars and VisualizationModel_._attributes properties.
JPanel.JPanel() and setting
opaque to be true.
Evolution to become inactive, then return.
Evolution to become inactive, then return.
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