ConstCRate

This collection of gene representation-independent functions
implements the population layer of extended evolutionary and genetic
algorithms and its support. The population layer consists of functions
for initializing, logging, observing, evaluating a population of genes,
as well as of computing the next population. For parallel evaluation of a
population of genes 4 execution models - named Sequential, MultiCore,
FutureApply, and Cluster - are provided. They are implemented by
configuring the lapply() function. The execution model FutureApply can be
externally configured as recommended by Bengtsson (2021)
<doi:10.32614/RJ-2021-048>. Configurable acceptance rules and cooling
schedules (see Kirkpatrick, S., Gelatt, C. D. J, and Vecchi, M. P. (1983)
<doi:10.1126/science.220.4598.671>, and Aarts, E., and Korst, J.
(1989, ISBN:0-471-92146-7) offer simulated annealing or greedy randomized
approximate search procedure elements. Adaptive crossover and mutation
rates depending on population statistics generalize the approach of
Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7).
For xega's architecture,
see Geyer-Schulz, A. (2025) <doi:10.5445/IR/1000187255>.

Andreas Geyer-Schulz

xegaPopulation

Genetic Population Level Functions

ConstCRate function

<dl><dt>fit</dt>
<dd>Fitness of gene.</dd>
<dt>lF</dt>
<dd>Local configuration.</dd></dl>

Arguments

Constant crossover rate. — ConstCRate

<dl>

<dt>fit</dt>
<dd>Fitness of gene.</dd>


<dt>lF</dt>
<dd>Local configuration.</dd>

</dl>

ConstCRate: Constant crossover rate.

Description

Usage

Value

Arguments

See Also

Examples