Package ec.gp.breed

# Class MutateDemotePipeline

java.lang.Object
ec.BreedingSource
ec.BreedingPipeline
ec.gp.GPBreedingPipeline
ec.gp.breed.MutateDemotePipeline

All Implemented Interfaces:
  `Prototype`, `Setup`, `SteadyStateBSourceForm`, `RandomChoiceChooserD`, `Serializable`, `Cloneable`

public class MutateDemotePipeline extends GPBreedingPipeline
MutateDemotePipeline works very similarly to the DemoteNode algorithm described in Kumar Chellapilla, "A Preliminary Investigation into Evolving Modular Programs without Subtree Crossover", GP98, and is also similar to the "insertion" operator found in Una-May O'Reilly's thesis, "An Analysis of Genetic Programming".

MutateDemotePipeline tries picks a random tree, then picks randomly from all the demotable nodes in the tree, and demotes one. If its chosen tree has no demotable nodes, or demoting its chosen demotable node would make the tree too deep, it repeats the choose-tree-then-choose-node process. If after tries times it has failed to find a valid tree and demotable node, it gives up and simply copies the individual.

"Demotion" means to take a node n and insert a new node m between n and n's parent. n becomes a child of m; the place where it becomes a child is determined at random from all the type-compatible slots of m. The other child slots of m are filled with randomly-generated terminals. Chellapilla's version of the algorithm always places n in child slot 0 of m. Because this would be unneccessarily restrictive on strong typing, MutateDemotePipeline instead picks the slot at random from all available valid choices.

A "Demotable" node means a node which is capable of demotion given the existing function set. In general to demote a node foo, there must exist in the function set a nonterminal whose return type is type-compatible with the child slot foo holds in its parent; this nonterminal must also have a child slot which is type-compatible with foo's return type.

This method is very expensive in searching nodes for "demotability". 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.`maxdepth` int >= 1 | (maximum valid depth of a mutated tree)
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

- ## Field Summary

  Fields
  Modifier and Type
  Field
  Description
  `static final int`
  `NUM_SOURCES`
  `static final String`
  `P_MAXDEPTH`
  `static final String`
  `P_MUTATEDEMOTE`
  `static final String`
  `P_NUM_TRIES`

  ### Fields inherited from class ec.gp.GPBreedingPipeline

  `P_NODESELECTOR, P_TREE, TREE_UNFIXED`

  ### Fields inherited from class ec.BreedingPipeline

  `DYNAMIC_SOURCES, likelihood, mybase, P_LIKELIHOOD, P_NUMSOURCES, P_SOURCE, sources, V_SAME, V_STUB`

  ### Fields inherited from class ec.BreedingSource

  `NO_PROBABILITY, P_PROB, probability`

- ## Constructor Summary

  Constructors
  Constructor
  Description
  `MutateDemotePipeline()`

- ## Method Summary

  Modifier and Type
  Method
  Description
  `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 subpopulation, ArrayList<Individual> inds, EvolutionState state, int thread, HashMap<String,Object> misc)`
  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 ec.gp.GPBreedingPipeline

  `produces`

  ### Methods inherited from class ec.BreedingPipeline

  `clone, fillStubs, finishProducing, individualReplaced, maxChildProduction, minChildProduction, preparePipeline, prepareToProduce, sourcesAreProperForm, typicalIndsProduced`

  ### Methods inherited from class ec.BreedingSource

  `getProbability, pickRandom, setProbability, setupProbabilities`

  ### Methods inherited from class java.lang.Object

  `equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

- ## Field Details

  - ### P_MUTATEDEMOTE

    public static final String P_MUTATEDEMOTE
    See Also:
      - Constant Field Values

  - ### P_NUM_TRIES

    public static final String P_NUM_TRIES
    See Also:
      - Constant Field Values

  - ### P_MAXDEPTH

    public static final String P_MAXDEPTH
    See Also:
      - Constant Field Values

  - ### NUM_SOURCES

    public static final int NUM_SOURCES
    See Also:
      - Constant Field Values

- ## Constructor Details

  - ### MutateDemotePipeline

    public MutateDemotePipeline()

- ## 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:
      - `Prototype.setup(EvolutionState,Parameter)`

  - ### 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`