You should run your selection hyper-heuristic implemented as part of your project to solve an unseen instance of the Open-top Bus Routing problem.

You should have already downloaded the unseen instance as part of the "Files required for practical test" archive. Locate the file "unseen-instance.obr" inside the instances folder and import it into your project, updating your code as required by your design so you can run your hyper-heuristic on this new instance.

You should run your hyper-heuristic on the unseen instance using:

• The RANDOM initialisation method.
• A runtime of 5 nominal minutes as benchmarked - either as done yourself if using your own machine or using that given on Moodle if using a lab machine.
• A single trial where your experimental seed is set to 20260508L.

 After running your hyper-heuristic, report only the objective value of the best solution found below.

When designing and implementing your selection hyper-heuristic, you may have attempted to improve its performance by performing some form of experimentation. 

Briefly explain (in two or three sentences):

• Which parameter(s) or algorithmic component(s) you tried to fine-tune,
• State which method(s) you used to do the tuning/configuration, and 
• Explain anything else you did that goes beyond tuning your hyper-heuristic on all of the given instances.

If you did not attempt to improve the performance of the hyper-heuristic beyond your original implementation, then you must leave your answer blank. 

In the textbox below, clearly state which heuristic selection mechanism and which move acceptance method you used when designing your submitted learning-based selection hyper-heuristic, and then briefly explain in one or two sentences how your heuristic selection method "learns" about the low-level heuristics and then "selects" which heuristic(s) to apply.

Given the following Open-top Bus Routing Problem (OBR) instance, calculate and state the objective value of the solution encoded by the following permutation representation: <2,0,1,3,4>.

NAME : understanding_instance
COMMENT : instance for the exam
BUS_DEPOT_LOCATION
-2 0
POINTS_OF_INTEREST
0 1
-1 1
-1 0
0 0
1 0
EOF

Before continuing with the following exercises, confirm that running your implementation of Iterated Local Search with the default experimental configuration yields the following plots. 

By using your implementation of Iterated Local Search, and appropriately configuring the framework, find a configuration for iIntensityOfMutation (IOM) and iDepthOfSearch (DOS) such that Iterated Local Search performs better than DBHC alone when comparing their median performances for solving MAX-SAT instance 1 with a 10 second computational time budget.

For full context, here is the boxplot produced when running DBHC for 31 trials on MAX-SAT instance 1 given 10 seconds of runtime using the seed of -1414736884 ("hill climbing".hashcode()) from the previous lab exercise:

The results of the previous exercise may have surprised you.

The best configuration in terms of obtaining the best median performance was either with IOM=1 and DOS=2, or with IOM=2 and DOS=2.

Now that you have finished implementing Iterated Local Search, look closely at the configuration of IntensityOfMutation and DepthOfSearch as specified in the Lab3ExercisesTestFrameConfig class. Select all of the following statements that we know are definitely true without having to perform any experimental analysis.

Given the box-plots for 3 algorithms A, B and C  based on the objective values obtained from 100 trials while solving an instance of a minimisation problem (and no other information), which of the following statement(s) about the search algorithms is definitely true?