evaluateFitness

population

[<code>function</code>]
Fitness function.

fitness.fun

[<code>ecr_optimization_task</code>]
Optimization task.

task

[<code>ecr_control</code>]
Control object containing all operators and further parameters.
See <code><a rd-options="" href="/link/setupECRControl?package=ecr&version=1.0.1" data-mini-rdoc="ecr::setupECRControl">setupECRControl</a></code> and <code><a rd-options="" href="/link/setupEvolutionaryOperators?package=ecr&version=1.0.1" data-mini-rdoc="ecr::setupEvolutionaryOperators">setupEvolutionaryOperators</a></code>.

control


This function expects a population, computes the fitness and
returns the matrix of fitness values. This function makes use of
<code><a rd-options="parallelMap" href="/link/parallelMap?package=ecr&version=1.0.1&to=parallelMap" data-mini-rdoc="parallelMap::parallelMap">parallelMap</a></code> to allow parallelization of fitness
evaluation.
Keep in mind, that the return value is a 1xn matrix in case of single-objective
optimization.


Provides a powerful framework for evolutionary
computing in R. The user can easily construct powerful evolutionary algorithms
for tackling both single- and multi-objective problems by plugging in different
predefined evolutionary building blocks, e. g., operators for mutation,
recombination and selection with just a few lines of code. Your problem
cannot be easily solved with a standard EA which works on real-valued vectors,
permutations or binary strings? No problem, 'ecr' has been developed with that in
mind. Extending the framework with own operators is also possible. Additionally
there are various comfort functions, like monitoring, logging and more.

Jakob Bossek

Evolutionary Computing in R

evaluateFitness function

[<code>ecr_control</code>]
Control object containing all operators and further parameters.
See <code><a rd-options='' href='setupECRControl'>setupECRControl</a></code> and <code><a rd-options='' href='setupEvolutionaryOperators'>setupEvolutionaryOperators</a></code>.


This function expects a population, computes the fitness and
returns the matrix of fitness values. This function makes use of
<code><a rd-options='parallelMap' href='parallelMap'>parallelMap</a></code> to allow parallelization of fitness
evaluation.
Keep in mind, that the return value is a 1xn matrix in case of single-objective
optimization.


evaluateFitness: Computes the fitness values for each member of a given population.

Description

Usage

Arguments

Value