Genetic Population Level Functions
Accepts only genes with equal or better fitness.
Configure the acceptance function of a genetic algorithm.
Individually Adaptive Metropolis Acceptance Rule.
Metropolis Acceptance Rule.
Accepts a new gene.
Configure the the execution model for gene evaluation.
Converts a population into a list of genetic operator pipelines.
Check terminatedFalse()
Check terminateError()
Check terminatePAC()
Configure consistency checks and adapt penv for terminationCondition...
Constant crossover rate.
Constant mutation rate.
Configure the cooling schedule of the acceptance function of a genetic...
Import for examples.
Import for examples.
Configure the crossover function of a genetic algorithm.
Exponential additive cooling.
Exponential multiplicative cooling.
Future apply of R-package future.apply.
Future apply of R-package future.apply configured for a tasks with h...
Individually adaptive crossover rate.
Individually adaptive mutation rate. (Bit mutation Rate)
Individually adaptive mutation rate.
Import for examples.
Import for examples.
Logarithmic multiplicative cooling.
MultiCore apply of library parallel.
MultiCore apply of library parallel for heterogenous tasks.
Metropolis acceptance probability.
Metropolis acceptance probability table.
Configure the mutation rate function of a genetic algorithm.
Power additive cooling.
Power multiplicative cooling.
uses parLapply of library parallel for using workers on machines in a ...
uses parLapplyLB of library parallel for using workers on machines in ...
Import for examples.
Terminates, if the absolute deviation from the global optimum is small...
No termination condition.
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 ...
Terminates, if the relative deviation from the global optimum is small...
Terminates if relative deviation from optimum is small. Works at 0.
Configure the termination condition(s) a genetic algorithm.
Trigonometric additive cooling.
Extracts indices of best genes in population.
Best solution in the population.
Remembers R command command with which algorithm has been called.
Evaluates a population of genes in a problem environment
Configures the evaluation of the population of a genetic algorithm.
Initializes a population of genes.
Combine fitness, generations, and the phenotype of the gene.
Computes the next population of genes.
Observe summary statistics of the fitness of the population.
Package xegaPopulation.
Repairs the list structure of a population of genes.
Evaluates a population of genes in a a problem environment repeatedly.
Provide elementary summary statistics of the fitness of the population...
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>.