Genetic Algorithm Optimization

Question

I asked a question a few weeks back regarding how one would do optimization in R(Optimizing for Vector Using Optimize R). Now that I have a proper grip with basic optimization in R, I would like to start employing GA's to solve for solutions.

Given the objective function:

div.ratio <- function(weight, vol, cov.mat){
  weight <- weight / sum(weight)
  dr <- (t(weight) %*% vol) / (sqrt(t(weight) %*% cov.mat %*% (weight)))  
  return(-dr)
}

I am using genalg package for optimizing, specifically the "rbga.bin" function. But the thing is one cannot seem to pass in more than one parameter, ie can't pass in vol and cov.mat. Am I missing something or understanding this incorrectly.

Edit: In the genalg package, there is a function called rbga.bin which is the one I am using.

Here is the simple code from previous question that can get you started:

rm(list=ls())
require(RCurl)
sit = getURLContent('https://github.com/systematicinvestor/SIT/raw/master/sit.gz',     binary=TRUE, followlocation = TRUE, ssl.verifypeer = FALSE)
con = gzcon(rawConnection(sit, 'rb'))
source(con)
close(con)
load.packages('quantmod')


data <- new.env()

tickers<-spl("VTI,VGK,VWO,GLD,VNQ,TIP,TLT,AGG,LQD")
getSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)
for(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)

bt.prep(data, align='remove.na', dates='1990::2013')

prices<-data$prices[,-10]  
ret<-na.omit(prices/mlag(prices) - 1)
vol<-apply(ret,2,sd)
cov.mat<-cov(ret)

out <- optim(par     = rep(1 / length(vol), length(vol)),  # initial guess
         fn      = div.ratio,
         vol     = vol,
         cov.mat = cov.mat,
         method  = "L-BFGS-B",
         lower   = 0,
         upper   = 1)

opt.weights <- out$par / sum(out$par) #optimal weights

While the above optim function works just fine, I was thinking if it is possible to reproduce this using a GA algorithm. So in the future if I am searching for multiple objectives I will be able to do this faster compared to GA. (I am not sure if it is faster, but this is the step to take to find out)

GAmodel <- rbga.bin(size = 7, #genes
popSize = 200, #initial number of chromosomes
iters = 100, #number of iterations
mutationChance = 0.01, #chance of mutation
evalFunc = div.ratio) #objective function

Doing the above seems to produce an error as div.ratio needs extra paramters, so I am looking for some help in structuring my problem so that it will be able to produce the optimal answer. I hope the above edit clarifies things.

Thanks

Answer 1

This is what you need:

GAmodel <- rbga(stringMin=rep(0, length(vol)), stringMax=rep(1, length(vol)),
popSize = 200,
iters = 100,
mutationChance = 0.01,
evalFunc = function(weight) div.ratio(weight, vol=vol, cov.mat=cov.mat))

(see first and last lines above).

The problems were:

1. vectors weight and vol must match lengths.

2. function evalFunc is called with a single parameter, causing the others to be missing. As I understand, you want to optimize in the weight vector only, keeping vol and cov.mat fixed.

3. If you want weight to be treated as a continuous variable, then use rbga instead.