ec.gp.breed
Class MutatePromotePipeline
java.lang.Object
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+--ec.BreedingSource
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+--ec.BreedingPipeline
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+--ec.gp.GPBreedingPipeline
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+--ec.gp.breed.MutatePromotePipeline
- All Implemented Interfaces:
- java.lang.Cloneable, Prototype, RandomChoiceChooser, java.io.Serializable, Setup, SteadyStateBSourceForm
- public class MutatePromotePipeline
- extends GPBreedingPipeline
MutatePromotePipeline works very similarly to the PromoteNode algorithm
described in Kumar Chellapilla,
"A Preliminary Investigation into Evolving Modular Programs without Subtree
Crossover", GP98, and is also similar to the "deletion" operator found in
Una-May O'Reilly's thesis,
"An Analysis of Genetic Programming".
MutatePromotePipeline tries tries times to find a tree
that has at least one promotable node. It then picks randomly from
all the promotable nodes in the tree, and promotes one. If it cannot
find a valid tree in tries times, it gives up and simply
copies the individual.
"Promotion" means to take a node n whose parent is m,
and replacing the subtree rooted at m with the subtree rooted at n.
A "Promotable" node means a node which is capable of promotion
given the existing type constraints. In general to promote a node foo,
foo must have a parent node, and must be type-compatible with the
child slot that its parent fills.
Typical Number of Individuals Produced Per produce(...) call
...as many as the source produces
Number of Sources
1
Parameters
base.tries
int >= 1 |
(number of times to try finding valid pairs of nodes) |
base.tree.0
0 < int < (num trees in individuals), if exists |
(tree chosen for mutation; if parameter doesn't exist, tree is picked at random) |
Default Base
gp.breed.mutate-demote
- See Also:
- Serialized Form
Method Summary |
Parameter |
defaultBase()
Returns the default base for this prototype. |
int |
numSources()
Returns the number of sources to this pipeline. |
int |
produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread)
Produces n individuals from the given subpopulation
and puts them into inds[start...start+n-1],
where n = Min(Max(q,min),max), where q is the "typical" number of
individuals the BreedingSource produces in one shot, and returns
n. |
void |
setup(EvolutionState state,
Parameter base)
Sets up the BreedingPipeline. |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
P_MUTATEPROMOTE
public static final java.lang.String P_MUTATEPROMOTE
P_NUM_TRIES
public static final java.lang.String P_NUM_TRIES
NUM_SOURCES
public static final int NUM_SOURCES
MutatePromotePipeline
public MutatePromotePipeline()
defaultBase
public Parameter defaultBase()
- Description copied from interface:
Prototype
- Returns the default base for this prototype.
This should generally be implemented by building off of the static base()
method on the DefaultsForm object for the prototype's package. This should
be callable during setup(...).
numSources
public int numSources()
- Description copied from class:
BreedingPipeline
- Returns the number of sources to this pipeline. Called during
BreedingPipeline's setup. Be sure to return a value > 0, or
DYNAMIC_SOURCES which indicates that setup should check the parameter
file for the parameter "num-sources" to make its determination.
- Overrides:
numSources
in class BreedingPipeline
setup
public void setup(EvolutionState state,
Parameter base)
- Description copied from class:
BreedingSource
- Sets up the BreedingPipeline. You can use state.output.error here
because the top-level caller promises to call exitIfErrors() after calling
setup. Note that probability might get modified again by
an external source if it doesn't normalize right.
The most common modification is to normalize it with some other
set of probabilities, then set all of them up in increasing summation;
this allows the use of the fast static BreedingSource-picking utility
method, BreedingSource.pickRandom(...). In order to use this method,
for example, if four
breeding source probabilities are {0.3, 0.2, 0.1, 0.4}, then
they should get normalized and summed by the outside owners
as: {0.3, 0.5, 0.6, 1.0}.
- Overrides:
setup
in class BreedingPipeline
- Following copied from class:
ec.BreedingSource
- See Also:
Prototype.setup(EvolutionState,Parameter)
produce
public int produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread)
throws java.lang.CloneNotSupportedException
- Description copied from class:
BreedingSource
- Produces n individuals from the given subpopulation
and puts them into inds[start...start+n-1],
where n = Min(Max(q,min),max), where q is the "typical" number of
individuals the BreedingSource produces in one shot, and returns
n. max must be >= min, and min must be >= 1. For example, crossover
might typically produce two individuals, tournament selection might typically
produce a single individual, etc.
- Overrides:
produce
in class BreedingSource