R: The representation of a gene in a chromosome for genetic algorithms

Gene {galgo}

R Documentation

The representation of a gene in a chromosome for genetic algorithms

Description

Represents the behaviour of a gene in a chromosome for the genetic algorithm. The default properties are supposed to be used in the variable selection problem for microarray data. However, they can be used for any other problem. In addition, any other wanted variable can be added.

See references for Genetic Algorithms.

Usage

Gene(id=0, shape1=0, shape2=0, generateFunc=runifInt, ...)

Arguments

`id`	To identify the object.
`shape1`	Parameter for a distribution. Used to generate a random value for a gene (mean, minimum, alfa, etc).
`shape2`	Parameter for a distribution. Used to generate a random value for a gene (sd, maximum, beta, etc).
`generateFunc`	Function that generate a random value for a gene using the above shape parameters. This function would be used to get an initial value and to mutate a gene. The default is a random uniform integer with shape1 as minimum and shape2 as maximum (either inclusive). The parameters used in the call are object, n, shape1, and shape2. The random value generated is not saved. If future values depends on the previous, you must save it explicitly in the object.
`...`	Other user named values to include in the object.

Class

Package: galgo
Class Gene

Object
~~|
~~+--Gene

Directly known subclasses:

public static class Gene
extends Object

Fields and Methods

Methods:

as.double Converts the gene parameters (shape1, shape2) to its numerical representation.

as.matrix Converts the gene parameters (shape1, shape2) to matrix.

generateRandom Generates a random value from the defined function.

mutate Mutates a gene.

newCollection Generates a list of cloned objects.

newRandomCollection Generates a list of cloned objects and random values.

print Prints the representation of a gene object.

reInit Erases all internal values in order to re-use the object.

summary Prints the representation of a gene object.

Methods inherited from Object:
as.list, unObject, $, $<-, [[, [[<-, as.character, attach, clone, detach, equals, extend, finalize, getFields, getInstanciationTime, getStaticInstance, hasField, hashCode, ll, load, objectSize, print, save

Author(s)

Victor Trevino. Francesco Falciani Group. University of Birmingham, U.K. http://www.bip.bham.ac.uk/bioinf

References

Goldberg, David E. 1989 Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Pub. Co. ISBN: 0201157675

Examples

  ge <- Gene(shape1=1, shape2=1000)
  ge

[Package galgo version 1.0-10 Index]

	`as.double`	Converts the gene parameters (shape1, shape2) to its numerical representation.
	`as.matrix`	Converts the gene parameters (shape1, shape2) to matrix.
	`generateRandom`	Generates a random value from the defined function.
	`mutate`	Mutates a gene.
	`newCollection`	Generates a list of cloned objects.
	`newRandomCollection`	Generates a list of cloned objects and random values.
	`print`	Prints the representation of a gene object.
	`reInit`	Erases all internal values in order to re-use the object.
	`summary`	Prints the representation of a gene object.