Updating sh examples for release

Examples/configuration_quality.sh

@@ -1,8 +1,8 @@
 #!/usr/bin/env bash
-# Auto-Generated .sh files from the original .md by Sparkle 0.8.8
+# Auto-Generated .sh files from the original .md by Sparkle 0.8.4
 
 ## Algorithm Quality Configuration
-# We can configure an algorithm too based on some quality objective, that can be defined by the user. To see all options for defining {ref}`SparkleObjective <sparkle-objective>`
+# We can configure an algorithm too based on some quality objective, that can be defined by the user. See the {ref}`SparkleObjective <sparkle-objective>` page for all options regarding objective defintions.
 # These steps can also be found as a Bash script in `Examples/configuration_qualty.sh`
 
 ### Initialise the Sparkle platform
@@ -11,20 +11,20 @@ sparkle initialise
 
 ### Add instances
 
-# Add train, and optionally test, instances (in this case for the VRP) in a given directory, without running solvers or feature extractors yet
+# Now we add train, and optionally test, instances for configuring our algorithm (in this case for the VRP). The instance sets are placed in a given directory.
 
 sparkle add_instances Examples/Resources/CVRP/Instances/X-1-10/
 sparkle add_instances Examples/Resources/CVRP/Instances/X-11-20/
 
 ### Add a configurable solver
 
-# Add a configurable solver (here for vehicle routing) with a wrapper containing the executable name of the solver and a string of command line parameters, without running the solver yet
+# Add a configurable solver (In this tutorial its an algorithm for vehicle routing) with a wrapper containing the executable name of the solver and a string of command line parameters.
 
-# The solver directory should contain the solver executable, the `sparkle_solver_wrapper.py` wrapper, and a `.pcs` file describing the configurable parameters
+# The solver directory should contain the `sparkle_solver_wrapper.py` wrapper, and a `.pcs` file describing the configurable parameters.
 
 sparkle add_solver Examples/Resources/CVRP/Solvers/VRP_SISRs/
 
-# If needed solvers can also include additional files or scripts in their directory, but keeping additional files to a minimum speeds up copying.
+# In this case the source directory also contains an executable, as the algorithm has been compiled from another programming language (`C++`). If needed solvers can also include additional files or scripts in their directory, but keeping additional files to a minimum speeds up copying.
 
 ### Configure the solver
 

Examples/configuration_randomforest.sh

@@ -1,5 +1,5 @@
 #!/usr/bin/env bash
-# Auto-Generated .sh files from the original .md by Sparkle 0.8.8
+# Auto-Generated .sh files from the original .md by Sparkle 0.8.4
 
 ## Configuring Random Forest on Iris
 

Examples/configuration_runtime.sh

@@ -1,5 +1,5 @@
 #!/usr/bin/env bash
-# Auto-Generated .sh files from the original .md by Sparkle 0.8.8
+# Auto-Generated .sh files from the original .md by Sparkle 0.8.4
 
 ## Algorithm Runtime Configuration
 

Examples/parallel_portfolio_runtime.sh

@@ -1,5 +1,5 @@
 #!/usr/bin/env bash
-# Auto-Generated .sh files from the original .md by Sparkle 0.8.8
+# Auto-Generated .sh files from the original .md by Sparkle 0.8.4
 
 ## Running a Parallel Portfolio
 # In this tutorial we will measure the runtime performance of several algorithms in parallel. The general idea is that we consider the algorithms as a portfolio that we run in parallel (hence the name) and terminate all running algorithms once a solution is found.

Examples/selection.sh

@@ -1,5 +1,5 @@
 #!/usr/bin/env bash
-# Auto-Generated .sh files from the original .md by Sparkle 0.8.8
+# Auto-Generated .sh files from the original .md by Sparkle 0.8.4
 
 ## Algorithm Selection
 

Examples/selection_multi-file_instance.sh

@@ -1,5 +1,5 @@
 #!/usr/bin/env bash
-# Auto-Generated .sh files from the original .md by Sparkle 0.8.8
+# Auto-Generated .sh files from the original .md by Sparkle 0.8.4
 
 ## Algorithm selection with multi-file instances