xegaDfGene: Package xegaDfGene.

In xegaDfGene: Gene Operations for Real-Coded Genes

Package xegaDfGene.

Description

Genetic operations for real-coded genetic and evolutionary algorithms.

Details

For real-coded genes, the xegaDfGene package provides

• Gene initialization.

• Decoding of parameters.

• Scaling functions as a function factory for configuration.

• Mutation functions as well as a function factory for configuration.

• Crossover functions as well as a function factory for configuration.

• Replication functions as well as a function factory for configuration.

Current support: Functions for differential evolution (de). See Price et al. (2005).

Real-Coded Gene Representation

A real-coded gene is a named list:

• $gene1: the gene must be a vector of reals.

• $fit: the fitness value of the gene (for EvalGeneDet and EvalGeneU) or the mean fitness (for stochastic functions evaluated with EvalGeneStoch).

• $evaluated: has the gene been evaluated?

• $evalFail: has the evaluation of the gene failed?

• $var: the cumulative variance of the fitness of all evaluations of a gene. (For stochastic functions)

• $sigma: the standard deviation of the fitness of all evaluations of a gene. (For stochastic functions)

• $obs: the number of evaluations of a gene. (For stochastic functions)

Abstract Interface of Problem Environment

We reuse the abstract interface of a problem environment for binary-coded genes. The number of parameters is given by length(penv$bitlength()).

A problem environment penv must provide:

• $f(parameters, gene, lF): Function with a real parameter vector as first argument which returns a gene with evaluated fitness.

• $genelength(): The number of bits of the binary coded real parameter vector. Used in InitGene.

• $bitlength(): A vector specifying the number of bits used for coding each real parameter. If penv$bitlength()[1] is 20, then parameters[1] is coded by 20 bits. Used in GeneMap.

• $lb(): The lower bound vector of each parameter. Used in GeneMap.

• $ub(): The upper bound vector of each parameter. Used in GeneMap.

The Architecture of the xegaX-Packages

The xegaX-packages are a family of R-packages which implement eXtended Evolutionary and Genetic Algorithms (xega). The architecture has 3 layers, namely the user interface layer, the population layer, and the gene layer:

• The user interface layer (package xega) provides a function call interface and configuration support for several algorithms: genetic algorithms (sga), permutation-based genetic algorithms (sgPerm), derivation-free algorithms as e.g. differential evolution (sgde), grammar-based genetic programming (sgp) and grammatical evolution (sge).

• The population layer (package xegaPopulation) contains population-related functionality as well as support for population statistics dependent adaptive mechanisms and parallelization.

• The gene layer is split into a representation-independent and a representation-dependent part:

  1. The representation-independent part (package xegaSelectGene) is responsible for variants of selection operators, evaluation strategies for genes, as well as profiling and timing capabilities.

  2. The representation-dependent part consists of the following packages:

     • xegaGaGene for binary coded genetic algorithms.

     • xegaPermGene for permutation-based genetic algorithms.

     • xegaDfGene for derivation-free algorithms as e.g. differential evolution.

     • xegaGpGene for grammar-based genetic algorithms.

     • xegaGeGene for grammatical evolution algorithms.

     The packages xegaDerivationTrees and xegaBNF support the last two packages: xegaBNF essentially provides a grammar compiler, and xegaDerivationTrees is an abstract data type for derivation trees.

Copyright

(c) 2023 Andreas Geyer-Schulz

License

MIT

<URL

https://github.com/ageyerschulz/xegaDfGene>

Installation

From CRAN by install.packages('xegaDfGene')

Author(s)

Andreas Geyer-Schulz

References

Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) The Differential Evolution Algorithm (Chapter 2), pp. 37-134. In: Differential Evolution. A Practical Approach to Global Optimization. Springer, Berlin. <doi:10.1007/3-540-31306-0>

See Also

Useful links:

• https://github.com/ageyerschulz/xegaDfGene

xegaDfGene documentation built on Aug. 22, 2025, 5:12 p.m.