remove soobench package

.travis.yml

@@ -16,7 +16,6 @@ install:
   - ./travis-tool.sh github_package berndbischl/ParamHelpers
   - ./travis-tool.sh github_package jakobbossek/smoof
   - ./travis-tool.sh github_package jimhester/covr
-  - ./travis-tool.sh r_install soobench
   - ./travis-tool.sh install_deps
 script: ./travis-tool.sh run_tests
 

DESCRIPTION

@@ -25,5 +25,4 @@ Imports:
     parallelMap (>= 1.1),
     reshape2 (>= 1.4.1)
 Suggests:
-    testthat,
-    soobench
+    testthat

README.md

@@ -17,15 +17,13 @@ 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 made available soon.
 
-The [soobench](http://cran.r-project.org/web/packages/soobench/index.html) R package provides a collection of different single objective test functions commonly used in algorithm benchmarking. As an example we are going to search for the global optimum of the one-dimensional Rastrigin function. The function definition is located in the soobench package, but *ecr* needs the objective function to be of [smoof](https://github.com/jakobbossek/smoof) type. Fortunately there is a function which generates an smoof function out of a soobench function.
+The [smoof](https://github.com/jakobbossek/smoof) R package provides a collection of different single objective test functions commonly used in algorithm benchmarking. As an example we are going to search for the global optimum of the one-dimensional Rastrigin function.
 
 ```splus
-library(soobench)
 library(smoof)
 library(ecr)
 
-obj.fun = makeSingleObjectiveFunctionFromSOOFunction("rastrigin", dimensions = 1L)
-
+obj.fun = makeRastriginFunction(dimensions = 1L)
 ```
 
 As a next step we generate an ecr *control object*, which holds all the neccessary parameters for the evolutionary algorithm. We decide ourself for 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. Gauss 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 representation float). Moreover we define a maximal number of 50 generations.

inst/examples/smoof_example.R

@@ -10,7 +10,7 @@
 library(methods)
 library(testthat)
 library(devtools)
-library(soobench)
+library(smoof)
 library(ggplot2)
 library(BBmisc)
 
@@ -38,11 +38,8 @@ myMonitorStep = function(envir = parent.frame()) {
 
 myMonitor = makeMonitor(step = myMonitorStep)
 
-# the soobench generator names changes in the recent dev version
-if (!exists("rastrigin_function", mode = "function")) {
-  rastrigin_function = generate_rastrigin_function
-}
-obj.fun = makeSingleObjectiveFunctionFromSOOFunction("rastrigin", dimensions = 1L)
+# generate objective function
+obj.fun = makeRastriginFunction(dimensions = 1L)
 
 # initialize control object
 control = ecr.control(
@@ -53,7 +50,7 @@ control = ecr.control(
   n.params = 1L,
   mutator.control = list(mutator.gauss.sd = 0.005),
   monitor = myMonitor,
-  stopping.conditions = list(makeMaximumIterationsStoppingCondition(max.iter = 25L))
+  stopping.conditions = setupStoppingConditions(max.iter = 25L)
 )
 
 # do the evolutionary magic