Crossover

average(ss::Vector{<:AbstractStrategy})

Returns the average value of the mutation parameter $\sigma$ of strategies ss.

average(population)

Returns an one offspring individual of a multi-parent recombination by averaging population.

marriage(population)

Returns an one offspring individual of a multi-parent recombination by random copying from population.

SPX(v1, v2)

Single point crossover between v1 and v2 individuals.

TPX(v1, v2)

Two point crossover between v1 and v2 individuals.

SHFX(v1, v2)

Shuffle crossover between the parents v1 and v2 that performs recombination similar to SPX preliminary shuffling these parents.

UX(v1, v2)

Uniform crossover between v1 and v2 individuals.

BINX(Cr::Real=0.5)

Returns a uniform (binomial) crossover function, see Recombination Interface, function with the probability Cr^[2].

The crossover probability value must be in unit interval, $Cr \in [0,1]$.

EXPX(Cr::Real=0.5)

Returns an exponential crossover function, see Recombination Interface, function with the probability Cr^[2].

The crossover probability value must be in unit interval, $Cr \in [0,1]$.

BSX(k::Int)

Binary Subset Crossover^[7]. Produces an offspring by first pooling the unique items of the two parents, and then creating each offspring by sampling without replacement at most k elements from the pool of items.

genop(v1, v2)

Returns the same parameter individuals v1 and v2 as an offspring pair (GENetic No OPeration).

DC(v1, v2)

Returns a randomly assembled offspring and its inverse from the elements of parents v1 and v2.

AX(v1, v2)

Average crossover generates an offspring by taking average of the parents v1 and v2.

WAX(w::Vector{<:Real})(v1, v2)

Returns a weighted average recombination operation, see Recombination Interface, which generate an offspring as weighted average of the parents v1 and v2 with the weights w.

IC(d::Real=0.0)

Returns an extended intermediate recombination operation, see Recombination Interface, which generates offspring u and v as

• $u_i = x_i + \alpha_i (y_i - x_i)$
• $v_i = y_i + \alpha_i (x_i - y_i)$

where $\alpha_i$ is chosen uniform randomly in the interval $[-d;d+1]$.

LC(d::Real=0.0)

Returns a extended line recombination operation, see Recombination Interface, which generates offspring u and v as

• $u_i = x_i + \alpha (y_i - x_i)$
• $v_i = y_i + \alpha (x_i - y_i)$

where $\alpha$ is chosen uniform randomly in the interval $[-d;d+1]$.

HX(x, y)

Heuristic crossover (HX) recombination operation^[3] generates offspring u and v as

• $u = x + r (x - y)$
• $v = y + r (y - x)$

where $r$ is chosen uniform randomly in the interval $[0;1)$.

LX(μ::Real = 0.0, b::Real = 0.2)

Returns a Laplace crossover (LX) recombination operation^[4], see Recombination Interface.

MILX(μ::Real = 0.0, b_real::Real = 0.15, b_int::Real = 0.35)

Returns a mixed integer Laplace crossover (MI-LX) recombination operation^[5], see Recombination Interface.

SBX(pm::Real = 0.5, η::Integer = 2)

Returns a Simulated Binary Crossover (SBX) recombination operation, see Recombination Interface, with the mutation probability pm of the recombinant component, and is the crossover distribution index η^[6].

PMX(v1, v2)

Partially mapped crossover which maps ordering and values information from the parents v1 and v2 to the offspring. A portion of one parent is mapped onto a portion of the other parent string and the remaining information is exchanged.

OX1(v1, v2)

Order crossover constructs an offspring by choosing a substring of one parent and preserving the relative order of the elements of the other parent.

CX(v1, v2)

Cycle crossover creates an offspring from the parents v1 and v2 such that every position is occupied by a corresponding element from one of the parents.

OX2(v1, v2)

Order-based crossover selects at random several positions in the parent v1, and the order of the elements in the selected positions of the parent v1 is imposed on the parent v2.

POS(v1, v2)

Position-based crossover is a modification of the OX1 operator. It selects a random set of positions in the parents v1 and v2, then imposes the position of the selected elements of one parent on the corresponding elements of the other parent.

SSX(v1, v2)

Subset crossover operator creates new offspring by pooling the unique indices of the two parent vectors v1 and v2, and then sampling a set of unique indices from this pool, uniformly at random.

crosstree(t1::Expr, t2::Expr)

Perform an arbitrary subtree swap between the expressions t1 and t2.