ga.crossover

Apply one- or two-point crossover to a selected population of binary chromosomes.

Automated population pharmacokinetic modeling framework for
data-driven initialisation, model evaluation, and metaheuristic
optimization. Supports genetic algorithms, ant colony optimization,
tabu search, and stepwise procedures for automated model selection
and parameter estimation within the nlmixr2 ecosystem.

Zhonghui Huang

nlmixr2auto

Automated Population Pharmacokinetic Modeling

Joseph Standing

Matthew Fidler

Frank Kloprogge

ga.crossover function

<dl><dt>sel.population</dt>
<dd>Numeric matrix of dimension npop by nbits. Each row is a
chromosome and is expected to contain binary values (0/1).</dd>
<dt>pcross</dt>
<dd>Single numeric value in \([0, 1]\) giving the probability of applying
crossover to each parent pair.</dd>
<dt>npop</dt>
<dd>Single positive even integer giving the population size.</dd>
<dt>nbits</dt>
<dd>Single positive integer giving the chromosome length.</dd></dl>

Arguments

Author

Crossover operator (one- or two-point) for binary chromosomes — ga.crossover

<dl>

<dt>sel.population</dt>
<dd>Numeric matrix of dimension npop by nbits. Each row is a
chromosome and is expected to contain binary values (0/1).</dd>


<dt>pcross</dt>
<dd>Single numeric value in \([0, 1]\) giving the probability of applying
crossover to each parent pair.</dd>


<dt>npop</dt>
<dd>Single positive even integer giving the population size.</dd>


<dt>nbits</dt>
<dd>Single positive integer giving the chromosome length.</dd>

</dl>

ga.crossover: Crossover operator (one- or two-point) for binary chromosomes

Description

Usage

Value

Arguments

Author

Details

Examples