galgo-package {galgo} | R Documentation

## Galgo perform feature selection from large scale 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 <vtrevino@itesm.mx>

### 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)

[Package galgo version 1.4 Index]