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License: MIT License
So far only perturbative search has been considered
Add those both as pre-built exploitable classes and as the code in the notebooks
TBC
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
Emperical analysis, search space structure. Chapters 4, 5
Multiple sampling modes:
Python and binary apps. Potentially R?
Different temperature schedules
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
Optionals:
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