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xegaPopulation (version 1.0.0.12)

Genetic Population Level Functions

Description

This collection of gene representation-independent functions implements the population layer of extended evolutionary and genetic algorithms and its support for the R-package 'xega' . 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) . Configurable acceptance rules and cooling schedules (see Kirkpatrick, S., Gelatt, C. D. J, and Vecchi, M. P. (1983) , 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) .

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install.packages('xegaPopulation')

Monthly Downloads

225

Version

1.0.0.12

License

MIT + file LICENSE

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Repository

https://github.com/ageyerschulz/xegaPopulation

Maintainer

Andreas Geyer-Schulz

Last Published

February 17th, 2026

Functions in xegaPopulation (1.0.0.12)

MutationRateFactory

Configure the mutation rate function of a genetic algorithm.

uses parLapply of library parallel for using workers on machines in a local network.

MultiCore apply of library parallel for heterogenous tasks.

uses parLapplyLB of library parallel for using workers on machines in a local network.

TerminationFactory

Configure the termination condition(s) a genetic algorithm.

MetropolisTable

Metropolis acceptance probability table.

MetropolisAcceptanceProbability

Metropolis acceptance probability.

TrigonometricAdditiveCooling

Trigonometric additive cooling.

Embeds genetic operator pipelines into the genes of a population.

checkTerminatePAC

Check terminatePAC()

checkTerminateError

Check terminateError()

checkTerminationFactory

Configure consistency checks and adapt penv for terminationConditions.

checkTerminatedFalse

Check terminatedFalse()

Converts a population into a list of genetic operator pipelines.

terminateAbsoluteError

Terminates, if the absolute deviation from the global optimum is small.

Import for examples.

xegaAsPipelineFactory

Configure asPipeline.

terminateRelativeErrorZero

Terminates if relative deviation from optimum is small. Works at 0.

terminatedFalse

No termination condition.

xegaBestGeneInPopulation

Extracts indices of best genes in population.

Terminates, if the solution is greater equal a threshold.

Terminates, if the solution is less equal a threshold.

Terminates if relative deviation from estimated PAC bound for optimum is small. Works at 0.

terminateRelativeError

Terminates, if the relative deviation from the global optimum is small.

Identity (No compilation of genetic operator pipelines for population).

Future apply of R-package future.apply.

futureLapplyHet

Future apply of R-package future.apply configured for a tasks with heterogenous execution times.

xegaEvalPopulation

Evaluates a population of genes in a problem environment

xegaConfiguration

Remembers R command command with which algorithm has been called.

Package xegaPopulation.

xegaLogEvalsPopulation

Combine fitness, generations, and the phenotype of the gene.

xegaNextPopulation

Computes the next population of genes.

xegaInitPopulation

Initializes a population of genes.

xegaBestInPopulation

Best solution in the population.

xegaEvalPopulationFactory

Configures the evaluation of the population of a genetic algorithm.

xegaRepEvalPopulation

Evaluates a population of genes in a a problem environment repeatedly.

Repairs the list structure of a population of genes.

xegaObservePopulation

Observe summary statistics of the fitness of the population.

xegaSummaryPopulation

Provide elementary summary statistics of the fitness of the population.

Configure the cooling schedule of the acceptance function of a genetic algorithm.

Constant mutation rate.

Accepts only genes with equal or better fitness.

AcceptMetropolis

Metropolis Acceptance Rule.

Import for examples.

Configure the acceptance function of a genetic algorithm.

Configure the the execution model for gene evaluation.

Accepts a new gene.

Constant crossover rate.

AcceptIVMetropolis

Individually Adaptive Metropolis Acceptance Rule.

Individually adaptive mutation rate. (Bit mutation Rate)

ExponentialMultiplicativeCooling

Exponential multiplicative cooling.

Individually adaptive crossover rate.

Import for examples.

Individually adaptive mutation rate.

LogarithmicMultiplicativeCooling

Logarithmic multiplicative cooling.

ExponentialAdditiveCooling

Exponential additive cooling.

MultiCore apply of library parallel.

Import for examples.

Import for examples.

PowerAdditiveCooling

Power additive cooling.

PowerMultiplicativeCooling

Power multiplicative cooling.

CrossRateFactory

Configure the crossover function of a genetic algorithm.