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xegaPopulation (version 1.0.0.11)

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. 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

1.0.0.11

License

MIT + file LICENSE

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Repository

https://github.com/ageyerschulz/xegaPopulation

Maintainer

Andreas Geyer-Schulz

Last Published

November 26th, 2025

Functions in xegaPopulation (1.0.0.11)

Converts a population into a list of genetic operator pipelines.

TrigonometricAdditiveCooling

Trigonometric additive cooling.

checkTerminateError

Check terminateError()

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

checkTerminatePAC

Check terminatePAC()

Future apply of R-package future.apply.

Import for examples.

terminateAbsoluteError

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

checkTerminationFactory

Configure consistency checks and adapt penv for terminationConditions.

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

checkTerminatedFalse

Check terminatedFalse()

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

xegaBestInPopulation

Best solution in the population.

Terminates, if the solution is greater equal a threshold.

Terminates, if the solution is less equal a threshold.

terminateRelativeErrorZero

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

futureLapplyHet

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

xegaBestGeneInPopulation

Extracts indices of best genes in population.

xegaConfiguration

Remembers R command command with which algorithm has been called.

terminateRelativeError

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

xegaEvalPopulation

Evaluates a population of genes in a problem environment

terminatedFalse

No termination condition.

xegaNextPopulation

Computes the next population of genes.

xegaRepEvalPopulation

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

xegaLogEvalsPopulation

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

xegaObservePopulation

Observe summary statistics of the fitness of the population.

xegaInitPopulation

Initializes a population of genes.

xegaSummaryPopulation

Provide elementary summary statistics of the fitness of the population.

xegaEvalPopulationFactory

Configures the evaluation of the population of a genetic algorithm.

Repairs the list structure of a population of genes.

Package xegaPopulation.

Accepts a new gene.

Constant mutation rate.

AcceptIVMetropolis

Individually Adaptive Metropolis Acceptance Rule.

Constant crossover rate.

Import for examples.

Configure the the execution model for gene evaluation.

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

Accepts only genes with equal or better fitness.

AcceptMetropolis

Metropolis Acceptance Rule.

Configure the acceptance function of a genetic algorithm.

Import for examples.

Individually adaptive mutation rate.

ExponentialAdditiveCooling

Exponential additive cooling.

LogarithmicMultiplicativeCooling

Logarithmic multiplicative cooling.

Individually adaptive mutation rate. (Bit mutation Rate)

Individually adaptive crossover rate.

MultiCore apply of library parallel.

CrossRateFactory

Configure the crossover function of a genetic algorithm.

Import for examples.

ExponentialMultiplicativeCooling

Exponential multiplicative cooling.

MetropolisTable

Metropolis acceptance probability table.

MutationRateFactory

Configure the mutation rate function of a genetic algorithm.

Import for examples.

TerminationFactory

Configure the termination condition(s) a genetic algorithm.

PowerAdditiveCooling

Power additive cooling.

PowerMultiplicativeCooling

Power multiplicative cooling.

MetropolisAcceptanceProbability

Metropolis acceptance probability.

MultiCore apply of library parallel for heterogenous tasks.