Genetic Algorithms
Binary encoding of decimal numbers and vice versa.
Gray encoding for binary strings
Class "de"
Differential Evolution via Genetic Algorithms
Class "ga"
Internal GA functions
Genetic Algorithms
Genetic Algorithms
Crossover operators in genetic algorithms
Mutation operators in genetic algorithms
Variable mutation probability in genetic algorithms
Population initialization in genetic algorithms
Selection operators in genetic algorithms
A function for setting or retrieving defaults genetic operators
Class "gaisl"
Islands Genetic Algorithms
Monitor genetic algorithm evolution
Summarize genetic algorithm evolution
Virtual Class "numericOrNA" - Simple Class for sub-assignment Values
Colours palettes
Parameters or decision variables names from an object of class `ga-cla...
Perspective plot with colour levels
Plot of Differential Evolution search path
Plot of Genetic Algorithm search path
Plot of Islands Genetic Algorithm search path
Summary for Differential Evolution
Summary for Genetic Algorithms
Summary for Islands Genetic Algorithms
Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e. a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach. For more details see Scrucca (2013) <doi:10.18637/jss.v053.i04> and Scrucca (2017) <doi:10.32614/RJ-2017-008>.