smc: Sinusoidal Motion Crossover (SMC)

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

Sinusoidal Motion Crossover (SMC)

Description

The proposed algorithm with the name of SMC is a simple algorithm that works deterministic and alternatively (Kumar & Panneerselvam, 2017).

Usage

smc(x1, x2, cxon, ...)

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
...	Further arguments passed to or from other methods.

Value

A matrix containing the generated offsprings.

Author(s)

Zeynel Cebeci & Erkut Tekeli

References

Varun Kumar, S.G. and Panneerselvam R. (2017). A Study of Crossover Operators for Genetic Algorithms to Solve VRP and its Variants and New Sinusoidal Motion Crossover Operator. International Journal of Computational Intelligence Research. Vol 13, Number 7, pp. 1717-1733

See Also

cross, 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

Examples

parent1 = c(1, 2, 3, 4, 5, 6, 7, 8) 
parent2 = c(4, 6, 7, 3, 2, 1, 8, 6)
smc(parent1, parent2)

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