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This is a generalization of the doAxisProjectionMutation. A subset QP (each point selected with independent probability pm) is selected. Next, a random intercept β0[0,1] is sampled. In a subsequent step a slope β1 is sampled uniformly at random from [0,3] (if β0<0.5) and [3,0] (if β00.5). This heuristic distinction of cases ensures that with high probability the resulting linear function β0+β1x runs inside the bounding-box [0,1]2 at least partially. Finally, all points pQ are modified by setting p2=β0+β1p1. Additionally, with probability p.jitter, Gaussian noise with mean zero and standard deviation jitter.sd is added to the second coordinate.

doLinearProjectionMutation(coords, pm = 0.1, p.jitter = 0,
  jitter.sd = 0, ...)

Arguments

coords

[matrix]
An n times 2 matrix of point coordinates in [0,1]2.

pm

[numeric(1)]
Probability of node mutation. Note that each node is subject to mutation independent of the the nodes. Default is 0.1, i.e., in expectation 10% of the points are mutated.

p.jitter

[numeric(1)]
Probability to add Gaussian noise to mutated points. Default is 0, i.e., no Gaussian noise at all.

jitter.sd

[numeric(1)]
Standard deviation for Gaussian noise. Defaults to 0.1. Note that this parameter has an effect only if p.jitter is greater zero.

...

[any]
Currently not used.

Value

[matrix] Mutated coordinates.

See also