IMPORTANT: This repository is outdated. Visit the official repository of ecr2 for the current state of development. There are various substantial interface changes and the package benefits from better performance as well as the possibility to prototype non-standard EAs by offering many more helper functions and make stuff much more explicit.
The ecr package provides a powerful framework for implementing both single- and multi-objective evolutionary algorithms in R. It has build-in support for standard genotypes like real-valued vectors, binary strings and permutations. Beside, different pre-defined building blocks in form of evolutionary operators (selectors, mutators, recombinators), e.g., k-tournament selection, uniform mutation, crossover and many helper functions frequently needed in evolutionary algorithms are provided. The possibility to extend the available toolbox by defining new operators and even operate on non-standard representations makes the package very flexible.
- Optimization of single and multi-objective functions.
- Build-in genotypes float, binary, permutation.
- Possibility to create custom representations/genotypes.
- Large collection of predefined evolutionary operators for standard representations.
- Powerful and flexible monitoring/logging.
- Event dispatching mechanism. For instance the user can register actions which should be called each time the population is updated.
The package is available at CRAN soon. Install the release version via:
install.packages("ecr")If you are interested in trying out and playing around with the current github developer version use the devtools package and type the following command in R:
devtools::install_github("jakobbossek/ecr")In this section we want to optimize a one dimensional function with an evolutionary algorithm using just the evolutionary operators shipped with the package. A more in-depth introduction will be available soon.
The smoof R package provides a large collection of continuous single-objective test functions commonly used in benchmarking algorithms. As an example we are going to search for the global optimum of the one-dimensional Rastrigin function.
library(smoof)
library(ggplot2)
library(ecr)
obj.fun = makeRastriginFunction(dimensions = 1L)
autoplot(obj.fun, show.optimum = TRUE)As a next step we generate an ecr control object, which holds all the neccessary parameters for the evolutionary algorithm. The construction of this object consists of generating the object itself and kind of decorating it with some evolutionary operators.
For our setup we choose the natural representation with real-valued numbers as the genotype, a population size of 20 individuals with 5 individuals being created by recombination and mutation in each generation. Furthermore we decide to use a 'plus' survival strategy, i. e., the current population and the offspring will be merged before survival selection takes place. Gaussian mutation with a standard deviance of 0.005 serves as the mutation operator and we keep the intermediate recombination operator (which is the default for the real-valued representation). Moreover we define a maximal number of 50 generations and activate the build-in logging which is needed in order to visualize the results afterwards.
# Generate the control object (set basic parameters)
control = setupECRControl(
n.population = 20L,
n.offspring = 10L,
representation = "float",
survival.strategy = "plus",
logger = setupOptPathLoggingMonitor(),
stopping.conditions = list(
setupMaximumIterationsTerminator(max.iter = 50L)
)
)
# Setup the evolutionary toolbox by specifying operators
control = setupEvolutionaryOperators(
control,
mutator = setupGaussMutator(sdev = 0.005)
)
print(control)Now lets start the optimization process and print the result object, which contains the optimization path, the best parameters, the best fitness value and some additional information.
set.seed(123)
res = doTheEvolution(obj.fun, control = control)
print(res)
print(head(as.data.frame(res$opt.path)))
print(autoplot(res, complete.trace = TRUE, log.fitness = TRUE))This example above demonstrated the most flexible way of creating an EA in ecr:
- Create optimization problem, i.e., the objective function to optimize.
- Set up an cntrol object with all the evolutionary parameters and operators attached to it.
- Pass both to the
doTheEvolutionFunctionfunction.
Although this approach is highly flexible in practise a R user often aims
to optimize a single-objective R function at hand. In this scenario the upper
low-level proceeding is cumbersome and a lot of code needs to be written.
The ecr package thus provides a more R like interface (similar to optim).
Alternative code for solving the Rastrigin function is shown below.
library(smoof)
library(ggplot2)
library(ecr)
obj.fun = makeRastriginFunction(dimensions = 1L)
res = ecr(obj.fun, n.dim = 1L, lower = -10, upper = 10,
n.population = 20L, n.offspring = 10L, representation = "float",
max.iter = 100L, mutator = setupGaussMutator(sdev = 0.005)
)Please address questions and missing features about the ecr package to the author Jakob Bossek [email protected]. Found some nasty bugs? Please use the issue tracker for this. Pay attention to explain the problem as good as possible. At its best you provide an example, so I can reproduce your problem quickly.
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