ec.gp.breed
Class MutateSwapPipeline
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.MutateSwapPipeline
- All Implemented Interfaces:
- java.lang.Cloneable, Prototype, RandomChoiceChooser, java.io.Serializable, Setup, SteadyStateBSourceForm
- public class MutateSwapPipeline
- extends GPBreedingPipeline
MutateSwapPipeline works very similarly to the Swap algorithm
described in Kumar Chellapilla,
"A Preliminary Investigation into Evolving Modular Programs without Subtree
Crossover", GP98.
MutateSwapPipeline picks a random tree, then picks
randomly from all the swappable nodes in the tree, and swaps two of its subtrees.
If its chosen tree has no swappable nodes, it repeats
the choose-tree process. If after tries times
it has failed to find a tree with swappable nodes, it gives up and simply
copies the individual.
"Swapping" means to take a node n, and choose two children
nodes of n, x and y, such that x's return
type is swap-compatible with y's slot, and y's return
type is swap-compatible with x's slot. The subtrees rooted at
x and y are swapped.
A "Swappable" node means a node which is capable of swapping
given the existing function set. In general to swap a node foo,
it must have at least two children whose return types are type-compatible
with each other's slots in foo.
This method is very expensive in searching nodes for
"swappability". However, if the number of types is 1 (the
GP run is typeless) then the type-constraint-checking
code is bypassed and the method runs a little faster.
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-swap
- 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_MUTATESWAP
public static final java.lang.String P_MUTATESWAP
P_NUM_TRIES
public static final java.lang.String P_NUM_TRIES
NUM_SOURCES
public static final int NUM_SOURCES
MutateSwapPipeline
public MutateSwapPipeline()
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