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So far only perturbative search has been considered

• ILS with Restart
• Memetic Algorithm
• Random Iterative Improvement

Add those both as pre-built exploitable classes and as the code in the notebooks

• Continuous
• 2-swap paths
• Constructive paths

TBC

• Population based, e.g. Genetic Algorithms
• Better ILS, e.g. Simulated Annealing

Allow for diffs in cost function: instead of calculating a cost function itself, calculate difference in cost between two solutions. Might decrease constant computation term, which is important for large instances of TSP specifically

This is to add various binary operations on state machines/graphs to combine GLSMs

TSP, Scheduling, Knapsack

DSL for defining GLSMs outside the code. Potential fallback to JSON/YAML

• Add KDoc/Dokka
• Host at jitpack

Emperical analysis, search space structure. Chapters 4, 5

Multiple sampling modes:

• Uniform, whole space
• Uniform, fixed neighbourhood
• Gaussians

• Must terminate (eventually potentially reach termination state)
• Must be fully connected
• Must not have transitions from the final state

Python and binary apps. Potentially R?

Different temperature schedules

• Linear
• Multiplicative
• Slow
  - [ ] Multiple steps -- same temperature

Starting with SAT and binary neighbourhood?

sequentialStateMachine<Double, BasicSolution<Double>> {
            step {
                name = "RandomWalk"
                strategy = RandomWalkStrategy(true)
            }
            terminationPredicate = FixedStepsPredicate(10)
        }

raises an exception

Exception in thread "main" java.lang.IllegalStateException: Required value was null.
	at space.d_lowl.kglsm.sls.StateMachineTransition$Builder.build(StateMachine.kt:36)
	at space.d_lowl.kglsm.sls.SequentialStateMachineBuilder.build(SequentialStateMachineBuilder.kt:109)

Add option for simulated annealing routine to stay at the same temperature for multiple iterations.

Current generic signatures are really cumbersome pretty much everywhere. We want as much of automatic inference as possible.

Implement if feasible and appropriate

To allow Evolutionary Algorithms on TSP

• Permutation search space
• 2-exchange neighbourhood
• TSP cost function
• Loading from file -- TSPlib

Optionals:

• k-exchange neighbourhood
• recombination and mutation operators for permutation search spaces