cross: Crossover

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

Crossover

Description

It is a wrapper function that calls crossover operators from a single function.

Usage

cross(crossfunc, matpool, cxon, cxpc, gatype, ...)

Arguments

crossfunc	The name of the crossover operator
matpool	A matrix. Mating pool containing selected individuals.
cxon	Number of offspring to be generated as a result of crossover
cxpc	Crossover Ratio. Default value is 0.95
gatype	Indicates the GA type. "gga" is assigned for generational refresh, and "ssga" for steady-state refresh.
...	Further arguments passed to or from other methods.

Value

A matrix containing the generated offsprings.

Author(s)

Zeynel Cebeci & Erkut Tekeli

References

Cebeci, Z. (2021). R ile Genetik Algoritmalar ve Optimizasyon Uygulamalari, 535 p. Ankara:Nobel Akademik Yayincilik.

See Also

px1, kpx, sc, rsc, hux, ux, ux2, mx, rrc, disc, atc, cpc, eclc, raoc, dc, ax, hc, sax, wax, lax, bx, ebx, blxa, blxab, lapx, elx, geomx, spherex, pmx, mpmx, upmx, ox, ox2, mpx, erx, pbx, pbx2, cx, icx, smc

Examples

genpop = initbin(12,8)                             #Initial population
m = ncol(genpop)-2                                 #Number of Gene
sumx = function(x, ...) (sum(x))                   #Fitness Function
fitvals = evaluate(fitfunc=sumx, genpop[,1:m])     #Fitness Values
genpop[,"fitval"] = fitvals
selidx = select(selfunc=selrws, fitvals)           #Selection of Parents
matpool = genpop[selidx,]                          #Mating Pool
offsprings = cross(crossfunc=px1, matpool=matpool, #Crossing
 cxon=2, cxpc=0.8, gatype="gga")
offsprings
offsprings = cross(crossfunc=kpx, matpool=matpool,
  cxon=2, cxpc=0.8, gatype="ssga", cxps=0.5, cxk=2)
offsprings

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