mlr-org · be-marc · Jul 8, 2025 · Jul 9, 2025 · Jul 9, 2025 · Jul 9, 2025
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -34,7 +34,7 @@ Imports:
     mlr3misc (>= 0.15.1),
     R6
 Suggests:
-    adagio,
+    cmaes,
     emoa,
     GenSA,
     irace (>= 4.0.0),

diff --git a/NEWS.md b/NEWS.md
@@ -1,5 +1,7 @@
 # bbotk (development version)
 
+* BREAKING CHANGE: Replace `adagio::pureCMAES()` with `cmaes::cma_es()` in `OptimizerBatchCmaes`.
+
 # bbotk 1.6.0
 
 # bbotk 1.5.0

diff --git a/R/OptimInstanceBatch.R b/R/OptimInstanceBatch.R
@@ -180,7 +180,6 @@ OptimInstanceBatch = R6Class("OptimInstanceBatch",
 
 objective_function = function(x, inst, direction) {
   xs = set_names(as.list(x), inst$search_space$ids())
-  inst$search_space$assert(xs)
   xdt = as.data.table(xs)
   res = inst$eval_batch(xdt)
   y = as.numeric(res[, inst$objective$codomain$target_ids, with = FALSE])

diff --git a/R/OptimizerBatchCmaes.R b/R/OptimizerBatchCmaes.R
@@ -4,34 +4,36 @@
 #' @name mlr_optimizers_cmaes
 #'
 #' @description
-#' `OptimizerBatchCmaes` class that implements CMA-ES. Calls [adagio::pureCMAES()]
-#' from package \CRANpkg{adagio}. The algorithm is typically applied to search
-#' space dimensions between three and fifty. Lower search space dimensions might
-#' crash.
+#' `OptimizerBatchCmaes` class that implements CMA-ES.
+#' Calls `cma_es()` from package \CRANpkg{cmaes}.
+#' The algorithm is typically applied to search space dimensions between three and fifty.
+#' Lower search space dimensions might crash.
 #'
 #' @templateVar id cmaes
 #' @template section_dictionary_optimizers
 #'
 #' @section Parameters:
 #' \describe{
-#' \item{`sigma`}{`numeric(1)`}
 #' \item{`start_values`}{`character(1)`\cr
-#' Create `"random"` start values or based on `"center"` of search space?
-#' In the latter case, it is the center of the parameters before a trafo is applied.
-#' If set to `"custom"`, the start values can be passed via the `start` parameter.}
+#'   Create `"random"` start values or based on `"center"` of search space?
+#'   In the latter case, it is the center of the parameters before a trafo is applied.
+#'   If set to `"custom"`, the start values can be passed via the `start` parameter.}
 #' \item{`start`}{`numeric()`\cr
-#' Custom start values. Only applicable if `start_values` parameter is set to `"custom"`.}
+#'   Custom start values.
+#'   Only applicable if `start_values` parameter is set to `"custom"`.}
 #' }
 #'
-#' For the meaning of the control parameters, see [adagio::pureCMAES()]. Note
-#' that we have removed all control parameters which refer to the termination of
-#' the algorithm and where our terminators allow to obtain the same behavior.
+#' For the meaning of the control parameters, see `cma_es()`.
+#' The parameters `maxit`, `stopfitness` and `stop.tolx` can be used additionally to our terminators.
+#' The default values of `maxit` is `100 * D^2` where `D` is the number of dimensions of the search space.
+#' The `stop.tolx` parameter stops when the step size is smaller than `1e-12 * sigma`.
+#' The `vectorized` parameter is always set to `TRUE`.
 #'
 #' @template section_progress_bars
 #'
 #' @export
 #' @examples
-#' if (requireNamespace("adagio")) {
+#' if (requireNamespace("cmaes")) {
 #'   search_space = domain = ps(
 #'     x1 = p_dbl(-10, 10),
 #'     x2 = p_dbl(-5, 5)
@@ -72,9 +74,26 @@ OptimizerBatchCmaes = R6Class("OptimizerBatchCmaes",
     #' Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function() {
       param_set = ps(
-        sigma = p_dbl(default = 0.5),
-        start_values = p_fct(default = "random", levels = c("random", "center", "custom")),
-        start = p_uty(default = NULL, depends = start_values == "custom")
+        fnscale       = p_dbl(default = 1),
+        maxit         = p_int(lower  = 1L),
+        stopfitness   = p_dbl(default = -Inf),
+        keep.best     = p_lgl(default = TRUE),
+        sigma         = p_uty(default = 0.5),
+        mu            = p_int(lower = 1L),
+        lambda        = p_int(lower = 1L),
+        weights       = p_uty(),
+        damps         = p_dbl(),
+        cs            = p_dbl(),
+        ccum          = p_dbl(),
+        ccov.1        = p_dbl(lower = 0),
+        ccov.mu       = p_dbl(lower = 0),
+        diag.sigma    = p_lgl(default = FALSE),
+        diag.eigen    = p_lgl(default = FALSE),
+        diag.pop      = p_lgl(default = FALSE),
+        diag.value    = p_lgl(default = FALSE),
+        stop.tolx     = p_dbl(), # undocumented stop criterion
+        start_values  = p_fct(default = "random", levels = c("random", "center", "custom")),
+        start         = p_uty(default = NULL, depends = start_values == "custom")
       )
       param_set$values$start_values = "random"
       param_set$values$start = NULL
@@ -84,7 +103,7 @@ OptimizerBatchCmaes = R6Class("OptimizerBatchCmaes",
         param_set = param_set,
         param_classes = "ParamDbl",
         properties = "single-crit",
-        packages = "adagio",
+        packages = "cmaes",
         label = "Covariance Matrix Adaptation Evolution Strategy",
         man = "bbotk::mlr_optimizers_cmaes"
       )
@@ -94,29 +113,32 @@ OptimizerBatchCmaes = R6Class("OptimizerBatchCmaes",
   private = list(
     .optimize = function(inst) {
       pv = self$param_set$values
+      start_values = pv$start_values
+      start = pv$start
 
-      if (pv$start_values == "custom") {
-        pv$par = pv$start
-        pv$start_values = NULL
-        pv$start = NULL
-      } else {
-        pv$par = search_start(inst$search_space, type = pv$start_values)
-        pv$start_values = NULL
-        pv$start = NULL
+      par = if (pv$start_values == "custom") set_names(start, inst$search_space$ids()) else search_start(inst$search_space, type = start_values)
+
+      if (length(par) < 2L) {
+        warning("CMA-ES is typically applied to search space dimensions between three and fifty. A lower search space dimension might crash.")
       }
 
-      pv$stopeval = .Machine$integer.max # make sure pureCMAES does not stop
-      pv$stopfitness = -Inf
+      control = pv[names(pv) %nin% c("start_values", "start")]
+      control$vectorized = TRUE
 
-      if (length(pv$par) < 2L) {
-        warning("CMA-ES is typically applied to search space dimensions between three and fifty. A lower search space dimension might crash.")
+      wrapper = function(xmat, inst) {
+        xdt = as.data.table(t(xmat))
+        res = inst$eval_batch(xdt)
+        y = res[, inst$objective$codomain$target_ids, with = FALSE][[1]]
+        y * inst$objective_multiplicator
       }
 
-      invoke(adagio::pureCMAES,
-        fun = inst$objective_function,
+      invoke(cmaes::cma_es,
+        par = par,
+        fn = wrapper,
         lower = inst$search_space$lower,
         upper = inst$search_space$upper,
-        .args = pv)
+        control = control,
+        inst = inst)
     }
   )
 )

diff --git a/man/mlr_optimizers_cmaes.Rd b/man/mlr_optimizers_cmaes.Rd
diff --git a/tests/testthat/_snaps/OptimizerCmaes.md b/tests/testthat/_snaps/OptimizerCmaes.md
@@ -5,8 +5,8 @@
     Output
 
       -- <OptimizerBatchCmaes> - Covariance Matrix Adaptation Evolution Strategy -----
-      * Parameters: start_values=random
+      * Parameters: mu=5, lambda=5, start_values=random
       * Parameter classes: <ParamDbl>
       * Properties: single-crit
-      * Packages: bbotk and adagio
+      * Packages: bbotk and cmaes
 
diff --git a/tests/testthat/test_OptimizerCmaes.R b/tests/testthat/test_OptimizerCmaes.R
@@ -1,5 +1,5 @@
 test_that("OptimizerBatchCmaes", {
-  skip_if_not_installed("adagio")
+  skip_if_not_installed("cmaes")
 
   search_space = domain = ps(
     x1 = p_dbl(-10, 10),
@@ -22,15 +22,15 @@ test_that("OptimizerBatchCmaes", {
     search_space = search_space,
     terminator = trm("evals", n_evals = 10L))
 
-  z = test_optimizer(instance, "cmaes", real_evals = 10L)
+  z = test_optimizer(instance, "cmaes", mu = 5, lambda = 5, real_evals = 10L)
 
   expect_class(z$optimizer, "OptimizerBatchCmaes")
   expect_snapshot(z$optimizer)
 
-  expect_error(test_optimizer_2d("cmaes", term_evals = 10L), "multi-crit objectives")
+  expect_error(test_optimizer_2d("cmaes",  mu = 5, lambda = 5, term_evals = 10L), "multi-crit objectives")
 
   instance$archive$clear()
-  optimizer = opt("cmaes", start_values = "custom", start = c(-9.1, 1.3))
+  optimizer = opt("cmaes",  mu = 5, lambda = 5, start_values = "custom", start = c(-9.1, 1.3))
   optimizer$optimize(instance)
   # start values are used for the initial mean vector so a deterministic test is not applicable
 })
-Original file line number
+Diff line change
@@ Expand Up / @@ -34,7 +34,7 @@ Imports: @@
         mlr3misc (>= 0.15.1),
         R6
     Suggests:
-        adagio,
+        cmaes,
         emoa,
         GenSA,
         irace (>= 4.0.0),
@@ Expand Down @@