Package ec.gp.breed

Class MutateSwapPipeline

java.lang.Object
ec.BreedingSource
ec.BreedingPipeline
ec.gp.GPBreedingPipeline
ec.gp.breed.MutateSwapPipeline
All Implemented Interfaces:
Prototype, Setup, SteadyStateBSourceForm, RandomChoiceChooserD, Serializable, Cloneable
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:

  • Field Details

  • Constructor Details

    • MutateSwapPipeline

      public MutateSwapPipeline()

  • Method Details

    • 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.
      Specified by:
      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}.

      Specified by:
      setup in interface Prototype
      Specified by:
      setup in interface Setup
      Overrides:
      setup in class BreedingPipeline
      See Also:

    • produce

      public int produce(int min, int max, int subpopulation, ArrayList<Individual> inds, EvolutionState state, int thread, HashMap<String,Object> misc)
      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.
      Specified by:
      produce in class BreedingSource