galgo-package: Galgo perform feature selection from large scale data.

In galgo: Genetic Algorithms for Multivariate Statistical Models from Large-Scale Functional Genomics Data

Description

Represents a genetic algorithm (GA) itself. The basic GA uses at least one population of chromosomes, a “fitness” function, and a stopping rule (see references).

The Galgo object is not limited to a single population, it implements a list of populations where any element in the list can be either a Niche object or a World object. Nervertheless, any user-defined object that implements evolve, progeny, best, max, bestFitness, and maxFitness methods can be part of the populations list.

The “fitness” function is by far the most important part of a GA, it evaluates a Chromosome to determine how good the chromosome is respect to a given goal. The function can be sensitive to data stored in .GlobalEnv or any other object (see *evaluate() for further details). For this package and in the case of the microarray, we have included several fitness functions to classify samples using different methods. However, it is not limited for a classification problem for microarray data, because you can create any fitness function in any given context.

The stopping rule has three options. First, it is simply a desired fitness value implemented as a numeric fitnessGoal, and If the maximum fitness value of a population is equal or higher than fitnessGoal the GA ends. Second, maxGenerations determine the maximum number of generations a GA can evolve. The current generation is increased after evaluating the fitness function to the entire population list. Thus, if the current generation reach maxGenerations the GA stops. Third, if the result of the user-defined callBackFunc is NA the GA stops. In addition, you can always break any R program using Ctrl-C (or Esc in Windows).

When the GA ends many values are used for futher analysis. Examples are the best chromosome (best method), its fitness (bestFitness method), the final generation (generation variable), the evolution of the maximum fitness (maxFitnesses list variable), the maximum chromosome in each generation (maxChromosome list variable), and the elapsed time (elapsedTime variable). Moreover, flags like goalScored, userCancelled, and running are available.

Details

Package:	galgo
Type:	Package
Version:	1.0
Date:	2011-05-31
License:	What license is it under?
LazyLoad:	yes

See BigBang and Galgo Objects for usage.

Author(s)

Victor Trevino and Francesco Falciani

Maintainer: Victor Trevino <[e-mail]>

References

GALGO: An R Package For Multivariate Variable Selection Using Genetic Algorithms Victor Trevino and Francesco Falciani School of Biosciences, University of Birmingham, Edgbaston, UK Bioinformatics 2006

See Also

BigBang and Galgo Objects.

Examples

## Not run: 
   bb <- configBB.VarSel(...) #not runs
   
## End(Not run)

galgo documentation built on May 2, 2019, 4:20 a.m.