config_algo

Create a configuration object for one of the algorithms available in EmiR. At minimum the id of the
algorithm (parameter <code>algorithm_id</code>), the number of iterations (parameter <code>iterations</code>) and the
number of individuals in the population (parameter <code>population_size</code>) have to be provided.

A C++ implementation of the following evolutionary
algorithms: Bat Algorithm (Yang, 2010 <doi:10.1007/978-3-642-12538-6_6>),
Cuckoo Search (Yang, 2009 <doi:10.1109/nabic.2009.5393690>),
Genetic Algorithms (Holland, 1992, ISBN:978-0262581110),
Gravitational Search Algorithm (Rashedi et al., 2009 <doi:10.1016/j.ins.2009.03.004>),
Grey Wolf Optimization (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>),
Harmony Search (Geem et al., 2001 <doi:10.1177/003754970107600201>),
Improved Harmony Search (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>),
Moth-flame Optimization (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>),
Particle Swarm Optimization (Kennedy et al., 2001 ISBN:1558605959),
Simulated Annealing (Kirkpatrick et al., 1983 <doi:10.1126/science.220.4598.671>),
Whale Optimization Algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>).
'EmiR' can be used not only for unconstrained optimization problems, but also
in presence of inequality constrains, and variables restricted to be integers.

Lorenzo Sostero

EmiR

Evolutionary Minimizer for R

Davide Pagano

config_algo function

<dl><dt>algorithm_id</dt>
<dd>id of the algorithm to be used. See list_of_algorithms for
the list of the available algorithms.</dd>
<dt>iterations</dt>
<dd>maximum number of iterations.</dd>
<dt>population_size</dt>
<dd>number of individuals in the population.</dd>
<dt>iterations_same_cost</dt>
<dd>maximum number of consecutive iterations with the same
(see the parameter <code>absolute_tol</code>) best cost before ending the minimization. If <code>NULL</code> the
minimization continues for the number of iterations specified by the parameter <code>iterations</code>.
Default is <code>NULL</code>.</dd>
<dt>absolute_tol</dt>
<dd>absolute tolerance when comparing best costs from consecutive iterations.
If <code>NULL</code> the machine epsilon is used. Default is <code>NULL</code>.</dd>
<dt>...</dt>
<dd>algorithm specific parameters (see specific configuration functions for more details).</dd></dl>

Arguments

Configuration object for algorithms — config_algo

<dl>

<dt>algorithm_id</dt>
<dd>id of the algorithm to be used. See list_of_algorithms for
the list of the available algorithms.</dd>


<dt>iterations</dt>
<dd>maximum number of iterations.</dd>


<dt>population_size</dt>
<dd>number of individuals in the population.</dd>


<dt>iterations_same_cost</dt>
<dd>maximum number of consecutive iterations with the same
(see the parameter <code>absolute_tol</code>) best cost before ending the minimization. If <code>NULL</code> the
minimization continues for the number of iterations specified by the parameter <code>iterations</code>.
Default is <code>NULL</code>.</dd>


<dt>absolute_tol</dt>
<dd>absolute tolerance when comparing best costs from consecutive iterations.
If <code>NULL</code> the machine epsilon is used. Default is <code>NULL</code>.</dd>


<dt>...</dt>
<dd>algorithm specific parameters (see specific configuration functions for more details).</dd>

</dl>

config_algo: Configuration object for algorithms

Description

Usage

Value

Arguments

Examples