| wax {adana} | R Documentation |
Whole Arithmetic Crossover
Description
New offspring are produced by applying an arithmetic mean to all of the parents' chromosomes. (Davis, 1985; Back et.al, 1991; Michalewicz & Janikov, 1991; Michalewicz, 1992; Michalewicz, 1995).
Usage
wax(x1, x2, cxon, cxalfa, ...)
Arguments
x1 |
A vector. It contains the chromosomal information of parent-1. |
x2 |
A vector. It contains the chromosomal information of parent-2. |
cxon |
Number of offspring to be generated as a result of crossover |
cxalfa |
Alpha value. If no value is entered, it is randomly selected by the function in the range [0,1]. |
... |
Further arguments passed to or from other methods. |
Value
A matrix containing the generated offsprings.
Author(s)
Zeynel Cebeci & Erkut Tekeli
References
Davis, L. (1985). Aplaying adaptive algorithms to epistatics domains. In Proc. of the Int. Joint Conf. on Artificial Intellengence, Vol. 85, pp. 162-164.
Back, T., Hoffmeister, F. and Schwefel, H.P. (1991). A survey of evolution strategies. In Proc. of the 4th Int. Conf. on Genetic Algorithms, pp. 2-9. Morgan Kaufmann.
Michalewicz, Z. and Janikov, S.J. (1991). Genetic algorithms for numerical optimization. Statistics and Computing, 1(2), 75-91.
Michalewicz, Z. (1992). Genetic algorithms + data structures = evolution programs. Berlin-Heidelberg: Springer Verlag.
Michalewicz, Z. (1995). Genetic algorithms, numerical optimization and constraints. In Proc. of the 4th Int. Conf. on Genetic Algorithms. pp. 151-158. Morgan Kaufmann.
See Also
cross,
px1,
kpx,
sc,
rsc,
hux,
ux,
ux2,
mx,
rrc,
disc,
atc,
cpc,
eclc,
raoc,
dc,
ax,
hc,
sax,
lax,
bx,
ebx,
blxa,
blxab,
lapx,
elx,
geomx,
spherex,
pmx,
mpmx,
upmx,
ox,
ox2,
mpx,
erx,
pbx,
pbx2,
cx,
icx,
smc
Examples
parent1 = c(1.1, 1.6, 0.0, 1.1, 1.4, 1.2)
parent2 = c(1.2, 0.0, 0.0, 1.5, 1.2, 1.4)
wax(parent1, parent2)