Bayesopt in title (#415)

Author: jakob-r

DESCRIPTION

@@ -1,17 +1,18 @@
 Package: mlrMBO
-Title: A Toolbox for Model-Based Optimization of Expensive Black-Box Functions
+Title: Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions
 Description: 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, parallel execution 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.
+    ('MBO'), also known as Bayesian optimization. It implements the Efficient
+    Global Optimization Algorithm and 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,
+    parallel execution 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")),
     person("Jakob", "Bossek", email = "[email protected]", role = "aut"),