The Simulated Binary Crossover was first proposed by [1]. It i suited for float representation only and creates two offspring. Given parents \(p_1, p_2\) the offspring are generated as \(c_{1/2} = \bar{x} \pm \frac{1}{2}\beta(p_2 - p_1)\) where \(\bar{x} = \frac{1}{2}(p_1 + p_2), p_2 > p_1\). This way \(\bar{c} = \bar{x}\) is assured.
recSBX(inds, eta = 5, p = 1, lower, upper)[ecr_recombinator]
[numeric]
Parents, i.e., list of exactly two numeric vectors of equal length.
[numeric(1)]
Parameter eta, i.e., the distance parameters of the crossover distribution.
[numeric(1)]
Crossover probability for each gene. Default is 1.0.
[numeric]
Vector of minimal values for each parameter of the decision space.
[numeric]
Vector of maximal values for each parameter of the decision space.
[1] Deb, K. and Agrawal, R. B. (1995). Simulated binary crossover for continuous search space. Complex Systems 9(2), 115-148.
Other recombinators:
recCrossover(),
recIntermediate(),
recOX(),
recPMX(),
recUnifCrossover()