gaSummary

A function which returns fitness summary statistics at each iteration of GA search.

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>.

Luca Scrucca

Genetic Algorithms

gaSummary function

<dl> <dt>x</dt>
<dd>a vector of fitness values for which summary statistics should be computed.</dd> <dt>...</dt>
<dd>further arguments passed to or from other methods.</dd></dl>

Arguments

Author

Summarize genetic algorithm evolution — gaSummary

<dl>

 <dt>x</dt>
<dd>a vector of fitness values for which summary statistics should be computed.</dd>

 <dt>...</dt>
<dd>further arguments passed to or from other methods.</dd>

</dl>

gaSummary: Summarize genetic algorithm evolution

Description

Usage

Value

Arguments

Author

Details

See Also