update description

Author: ja-thomas

DESCRIPTION

@@ -1,10 +1,16 @@
 Package: mlrMBO
-Title: Model-Based Optimization for mlr
-Description: A framework for the (sequential) model-based parameter
-    optimization. It offers methods to optimize numeric or discrete influence
-    parameters of non-linear black-box single- or multi-objective target
-    functions like an industrial simulator or a time-consuming algorithm using
-    cheap surrogate models.
+Title: mlrMBO: A Toolbox for Model-Based Optimization of Expensive Black-Box Functions
+Description: mlrMBO is a flexible and comprehensive R toolbox for model-based
+optimization (MBO), also known as Bayesian optimization. It is designed for
+both single- and multi-objective optimization with mixed continuous, categorical
+and conditional parameters. The machine learning toolbox mlr provide dozens
+of regression learners to model the performance of the target algorithm with
+respect to the parameter settings. It provides many different infill criteria to
+guide the search process. Additional features include multi-point batch proposal,
+parallelization as well as visualization and sophisticated logging mechanisms,
+which is especially useful for teaching and understanding of algorithm behavior.
+mlrMBO is implemented in a modular fashion, such that single components can
+be easily replaced or adapted by the user for specific use cases.
 Authors@R: c(
     person("Bernd", "Bischl", email = "[email protected]", role = c("aut", "cre")), 
     person("Jakob", "Bossek", email = "[email protected]", role = "aut"),