Class GeneticProgramming

java.lang.Object

org.episteme.natural.computing.ai.evolutionary.GeneticProgramming

public class GeneticProgramming
extends Object

Basic genetic programming framework. *

Reference:
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.

Since:

1.0

Author:

Silvere Martin-Michiellot, Gemini AI (Google DeepMind)

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	GeneticProgramming.ConstNode	Constant node.
static class	GeneticProgramming.Node	Represents an expression tree node.
static class	GeneticProgramming.OpNode	Binary operator node.
static class	GeneticProgramming.VarNode	Variable node (x).

Constructor Summary

Constructors

Constructor	Description
GeneticProgramming()

Method Summary

Modifier and Type	Method	Description
GeneticProgramming.Node	evolve(double[] inputs, double[] outputs, int populationSize, int generations)	Simple evolutionary run.
double	fitness(GeneticProgramming.Node tree, double[] inputs, double[] outputs)	Evaluates fitness as negative mean squared error.
GeneticProgramming.Node	randomTree(int depth)	Generates a random expression tree.
void	setCrossoverRate(double rate)
void	setMaxDepth(int depth)
void	setMutationRate(double rate)

Methods inherited from class Object

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

Constructor Details

GeneticProgramming

public GeneticProgramming()

Method Details

setMaxDepth

public void setMaxDepth(int depth)

setMutationRate

public void setMutationRate(double rate)

setCrossoverRate

public void setCrossoverRate(double rate)

randomTree

public GeneticProgramming.Node randomTree(int depth)

Generates a random expression tree.

fitness

public double fitness(GeneticProgramming.Node tree,
 double[] inputs,
 double[] outputs)

Evaluates fitness as negative mean squared error.

evolve

public GeneticProgramming.Node evolve(double[] inputs,
 double[] outputs,
 int populationSize,
 int generations)

Simple evolutionary run.