The fitness function, which should take a
vector as argument and return a numeric value (See
details).
n
The number of elements to permutate.
popSize
The population size.
mutRate
The mutation rate, a numeric value between
0 and 1. When implementing a custom mutation function,
this value should be one of the parameters (see details
and examples).
cxRate
The crossover rate, a numeric value between
0 and 1. This parameter specifies the probability of two
individuals effectively exchange DNA during crossover. In
case the individuals didn't crossover, the offspring is a
exact copy of the parents. When implementing a custom
crossover function, this value should be one of the
arguments (see details and examples).
eliteRate
A numeric value between 0 and 1. The
eliteRate * popSize best-fitted individuals will
automatically be selected for the next generation.
selection
The selection operator to be used. You
can also implement a custom selection function (see
details and examples).
crossover
The crossover operator to be used. You
can also implement a custom crossover function (see
details and examples).
mutation
The mutation operator to be used. You can
also implement a custom mutation function (see details
and examples).
Details
This is the function used to configure and fine-tune a
permutation-based optimization. The basic usage requires
only the FUN parameter (function to be maximized),
together with n (the number of elements to
permutate), all the other parameters have sensible
defaults.
The parameters selection, crossover and
mutation can also take a custom function as
argument, which needs to be in the appropriate format
(see the examples). The text below explains the default
behaviour for these parameters, which will be usefull if
you want to override one or more genetic operators.
selection: The fitness
option performs a fitness-proportionate selection,
so that the fittest individuals will have greater chances
of being selected. If you choose this option, the value
returned by FUN (the fitness value) should be
non-negative. The uniform option will
randomly sample the individuals to mate, regardless of
their fitness value. See the examples if you want to
implement a custom selection function.
crossover: The pmx option
will perform a 'partially mapped crossover' of the
individuals DNA. See the references and examples if you
need to implement a custom crossover function. The trick
with permutation crossover is to make sure that the
resulting children are valid permutations.
mutation: The swap option
will perform a simple swap between specific gene
positions, according to the mutation rate specified.
Value
An object of class GAPerm, which you can pass as
an argument to plot or summary. This object
is a list with the following accessor functions:
bestFit:
Returns a vector with
the best fitness achieved in each generation.
meanFit:
Returns a vector with the mean
fitness achieved in each generation.
bestIndividual:
Returns a vector with the
best solution found.
evolve(h):
This is
the function you call to evolve your population.
You also need to specify the number of generations to
evolve.
population:
Returns the current
population matrix.
References
Even, S. Algorithmic Combinatorics. The Macmillan
Company, NY 1973.