diff --git a/NAMESPACE b/NAMESPACE
index 8b35d48..c13a73a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -11,6 +11,7 @@ S3method(print,ecr_optimization_task)
 S3method(print,ecr_result)
 export(approximateIdealPoint)
 export(approximateNadirPoint)
+export(asemoa)
 export(computeAverageHausdorffDistance)
 export(computeCrowdingDistance)
 export(computeDistanceFromPointToSetOfPoints)
diff --git a/R/emoa.as-emoa.R b/R/emoa.as-emoa.R
new file mode 100644
index 0000000..41ce641
--- /dev/null
+++ b/R/emoa.as-emoa.R
@@ -0,0 +1,161 @@
+#' @title
+#'   Implementation of the NSGA-II EMOA algorithm by Deb.
+#'
+#' @description
+#'   The AS-EMOA, short for aspiration set evolutionary multi-objective algorithm,
+#'   aims to incorporate expert knowledge into multi-objective optimization [1].
+#'   The algorithm expects an aspiration set, i.e., a set of reference points. It
+#'   then creates an appriximation of the pareto front close to the aspiration set
+#'   utilizing the average Hausdorff distance.
+#'
+#' @note
+#'   This is a pure R implementation of the NSGA-II algorithm. It hides the regular
+#'   \pkg{ecr} interface and offers a more R like interface while still being quite
+#'   adaptable.
+#'
+#' @references Rudolph, G., Schuetze, S., Grimme, C., Trautmann, H: An Aspiration Set
+#'   EMOA Based on Averaged Hausdorff Distances. LION 2014: 153-156.
+#'
+#' @param task [\code{ecr_optimization_task} | \code{smoof_function}]\cr
+#'   Optimization task or objective function of type \code{smoof_function}.
+#' @param n.population [\code{integer(1)}]\cr
+#'   Population size. Default is \code{100}.
+#' @param n.offspring [\code{integer(1)}]\cr
+#'   Offspring size, i.e., number of individuals generated by variation operators
+#'   in each iteration. Default is \code{n.population}.
+#' @param aspiration.set [\code{matrix}]\cr
+#'   The aspiration set. Each column contains one point of the set.
+#' @param n.archive [\code{integer(1)}]\cr
+#'   Size of the pareto archive, i.e., the number of nondominated points which we
+#'   aim to generate. Default is \code{ncol(aspiration.set)}.
+#' @template arg_parent_selector
+#' @template arg_mutator
+#' @template arg_recombinator
+#' @param max.iter [\code{integer(1)}]\cr
+#'   Maximal number of iterations. Default ist \code{100L}.
+#' @param max.evals [\code{integer(1)}]\cr
+#'   Maximal number of iterations/generations. Default is \code{Inf}.
+#' @param max.time [\code{integer(1)}]\cr
+#'   Time budget in seconds. Default ist \code{Inf}.
+#' @return [\code{ecr_ecr_multi_objective_result}]
+#' @export
+asemoa = function(
+  task,
+  n.population = 100L, n.offspring = n.population,
+  aspiration.set = NULL,
+  n.archive,
+  parent.selector = makeSimpleSelector(),
+  mutator = makeGaussMutator(),
+  recombinator = makeCrossoverRecombinator(),
+  max.iter = 100L,
+  max.evals = NULL,
+  max.time = NULL) {
+
+  if (isSmoofFunction(task)) {
+    task = makeOptimizationTask(task)
+  }
+  assertMatrix(aspiration.set, mode = "numeric", any.missing = FALSE, all.missing = FALSE, min.rows = 2L)
+  if (nrow(aspiration.set) != task$n.objectives) {
+    stopf("AS-EMAO: Dimension of the aspiration set needs to be equal to the number of objectives,
+      but %i <> %i.", nrow(aspiration.set), task$n.objectives)
+  }
+  if (is.null(n.archive)) {
+    n.archive = ncol(aspiration.set)
+  }
+  assertInt(n.archive, na.ok = FALSE, lower = 2L)
+
+  # This is the main selection mechanism of the AS-EMOA.
+  # Remove the point which leads to highest
+  deltaOneUpdate = function(set, aspiration.set) {
+    # here we need to apply this strange information. See the reference for details
+    # yeah, I could use range here but it is more readable this way
+    min1 = min(aspiration.set[1L, ])
+    min2 = min(aspiration.set[2L, ])
+    max1 = max(aspiration.set[1L, ])
+    max2 = max(aspiration.set[2L, ])
+
+    # transform
+    set[1L, ] = (set[1L, ] - min1) / (max2 - min2)
+    set[2L, ] = (set[2L, ] - min2) / (max1 - min1)
+
+    return(computeAverageHausdorffDistance(set, aspiration.set))
+  }
+
+  # Implementation of surival selection operator of the AS-EMOA algorithm.
+  asemoaSelector = makeSelector(
+    selector = function(population, storage, n.select, control) {
+      fitness = population$fitness
+      population = population$individuals
+
+      # filter nondominated points
+      nondom.idx = which.nondominated(fitness)
+      population = population[nondom.idx]
+      fitness = fitness[, nondom.idx, drop = FALSE]
+
+      n.archive = control$n.archive
+      # if maximal number of individuals is not exceeded yet
+      # simply return
+      if (length(population) <= n.archive) {
+        return(makePopulation(population, fitness))
+      }
+
+      # Otherwise we need to do the computationally more expensive part
+      hausdorffDistances = lapply(seq(length(population)), function(idx) {
+        deltaOneUpdate(fitness[, -idx, drop = FALSE], control$aspiration.set)
+      })
+
+      #FIXME: here we need to check if there are multiple elements with this distance
+      tmp = getMinIndex(hausdorffDistances)
+
+      return(makePopulation(population[-tmp], fitness[, -tmp, drop = FALSE]))
+    },
+    supported.objectives = "multi-objective",
+    name = "AS-EMOA selector",
+    description = "Selection takes place based on (modified) average Hausdorff metric"
+  )
+
+  asemoaGenerator = makeGenerator(
+    generator = function(size, control) {
+      uniformGenerator = makeUniformGenerator()
+      population = uniformGenerator(size, control)
+      #NOTE: here we use the objective function to compute the fitness values
+      fitness = computeFitness(population, task$fitness.fun)
+      # now filter out dominated solutions
+      nondom.idx = which.nondominated(fitness)
+      population$individuals = population$individuals[nondom.idx]
+      return(population)
+    },
+    name = "AS-EMOA generator",
+    description = "Generates uniformaly and reduces to non-dominated set",
+    supported = "float"
+  )
+
+  # AS-EMOA control object
+  ctrl = setupECRControl(
+    n.population = n.population,
+    n.offspring = n.offspring,
+    representation = "float",
+    monitor = makeConsoleMonitor(),
+    stopping.conditions = list(
+      makeMaximumEvaluationsStoppingCondition(max.evals),
+      makeMaximumTimeStoppingCondition(max.time),
+      makeMaximumIterationsStoppingCondition(max.iter)
+    )
+  )
+
+  ctrl = setupEvolutionaryOperators(
+    ctrl,
+    parent.selector = parent.selector,
+    recombinator = recombinator,
+    generator = asemoaGenerator,
+    mutator = mutator,
+    survival.selector = asemoaSelector
+  )
+
+  #FIXME: this is rather ugly. We simply add some more args to the control object
+  # without sanity checks and stuff like that.
+  ctrl$n.archive = n.archive
+  ctrl$aspiration.set = aspiration.set
+
+  return(doTheEvolution(task, ctrl))
+}
diff --git a/man/asemoa.Rd b/man/asemoa.Rd
new file mode 100644
index 0000000..356282b
--- /dev/null
+++ b/man/asemoa.Rd
@@ -0,0 +1,68 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/emoa.as-emoa.R
+\name{asemoa}
+\alias{asemoa}
+\title{Implementation of the NSGA-II EMOA algorithm by Deb.}
+\usage{
+asemoa(task, n.population = 100L, n.offspring = n.population,
+  aspiration.set = NULL, n.archive, parent.selector = makeSimpleSelector(),
+  mutator = makeGaussMutator(), recombinator = makeCrossoverRecombinator(),
+  max.iter = 100L, max.evals = NULL, max.time = NULL)
+}
+\arguments{
+\item{task}{[\code{ecr_optimization_task} | \code{smoof_function}]\cr
+Optimization task or objective function of type \code{smoof_function}.}
+
+\item{n.population}{[\code{integer(1)}]\cr
+Population size. Default is \code{100}.}
+
+\item{n.offspring}{[\code{integer(1)}]\cr
+Offspring size, i.e., number of individuals generated by variation operators
+in each iteration. Default is \code{n.population}.}
+
+\item{aspiration.set}{[\code{matrix}]\cr
+The aspiration set. Each column contains one point of the set.}
+
+\item{n.archive}{[\code{integer(1)}]\cr
+Size of the pareto archive, i.e., the number of nondominated points which we
+aim to generate. Default is \code{ncol(aspiration.set)}.}
+
+\item{parent.selector}{[\code{ecr_selector}]\cr
+Selection operator which implements a procedure to copy individuals from a
+given population to the mating pool, i. e., allow them to become parents.}
+
+\item{mutator}{[\code{ecr_mutator}]\cr
+Mutation operator of type \code{ecr_mutator}.}
+
+\item{recombinator}{[\code{ecr_recombinator}]\cr
+Recombination operator of type \code{ecr_recombinator}.}
+
+\item{max.iter}{[\code{integer(1)}]\cr
+Maximal number of iterations. Default ist \code{100L}.}
+
+\item{max.evals}{[\code{integer(1)}]\cr
+Maximal number of iterations/generations. Default is \code{Inf}.}
+
+\item{max.time}{[\code{integer(1)}]\cr
+Time budget in seconds. Default ist \code{Inf}.}
+}
+\value{
+[\code{ecr_ecr_multi_objective_result}]
+}
+\description{
+The AS-EMOA, short for aspiration set evolutionary multi-objective algorithm,
+  aims to incorporate expert knowledge into multi-objective optimization [1].
+  The algorithm expects an aspiration set, i.e., a set of reference points. It
+  then creates an appriximation of the pareto front close to the aspiration set
+  utilizing the average Hausdorff distance.
+}
+\note{
+This is a pure R implementation of the NSGA-II algorithm. It hides the regular
+  \pkg{ecr} interface and offers a more R like interface while still being quite
+  adaptable.
+}
+\references{
+Rudolph, G., Schuetze, S., Grimme, C., Trautmann, H: An Aspiration Set
+  EMOA Based on Averaged Hausdorff Distances. LION 2014: 153-156.
+}
+