hgaoptimx: GA + optimx hybridization function

In adana: Adaptive Nature-Inspired Algorithms for Hybrid Genetic Optimization

GA + optimx hybridization function

Description

This function allows GA to hybridize with methods in the optimx package (Nash & Varadhan, 2011; Nash, 2014).

Usage

hgaoptimx(genpop, fitfunc, hgaparams,
                     hgaftype, hgans, ...)

Arguments

genpop	A matrix of individuals in the current population and their fitness values.
fitfunc	Fitness function
hgaparams	A list of parameters defined for use by the Optim function.
hgaftype	Types of fitness to transfer. w: individuals with the worst fitness value b: individuals with the best fitness value r: randomly selected individuals
hgans	Number of individuals to be transferred to the Optim.
...	Further arguments passed to or from other methods.

Value

A matrix containing the updated population.

Author(s)

Zeynel Cebeci & Erkut Tekeli

References

Nash, J.C. and Varadhan, R. (2011). Unified optimization algorithms to aid software system users: optimx for R. Journal of Statistical Software, 43(9), 1-14. URL http://www.jstatsoft.org/v43/i09. Nash, J.C. (2014). On best practice optimization methods in R. Journal of Statistical Software, 60(2), 1-14. URL http://www.jstatsoft.org/v60/i02.

See Also

hgaoptim, hgaroi

Examples

n = 5                                # Size of population 
m = 2                                # Number of Variables
lb = c(-5.12, -5.12)                 # Lower bounds of sample data
ub = c(5.12, 5.12)                   # Upper bounds of sample data
hgaparams = list(method="L-BFGS-B", 
  poptim=0.05, pressel=0.5,
  lower=lb, upper=ub,
  control=list(maximize=FALSE, maxit=100))
genpop = initval(n, m, lb=lb, ub=ub) # Sample population
fitfunc = function(x, ...) 2*(x[1]-1)^2 + 5*(x[2]-2)^2 + 10
fitvals = evaluate(fitfunc, genpop[,1:m])
genpop[,"fitval"]=fitvals
genpop
genpop = hgaoptimx(genpop, fitfunc, hgaparams, hgaftype="r", hgans=3)
genpop

adana documentation built on March 18, 2022, 6:03 p.m.