A survey of methods for automated algorithm configuration
Algorithm configuration (AC) is concerned with the automated search of the most suitable
parameter configuration of a parametrized algorithm. There is currently a wide variety of AC …
parameter configuration of a parametrized algorithm. There is currently a wide variety of AC …
[HTML][HTML] Automated streamliner portfolios for constraint satisfaction problems
Constraint Programming (CP) is a powerful technique for solving large-scale combinatorial
problems. Solving a problem proceeds in two distinct phases: modelling and solving …
problems. Solving a problem proceeds in two distinct phases: modelling and solving …
A framework for generating informative benchmark instances
Benchmarking is an important tool for assessing the relative performance of alternative
solving approaches. However, the utility of benchmarking is limited by the quantity and …
solving approaches. However, the utility of benchmarking is limited by the quantity and …
Discriminating instance generation from abstract specifications: A case study with CP and MIP
We extend automatic instance generation methods to allow cross-paradigm comparisons.
We demonstrate that it is possible to completely automate the search for benchmark …
We demonstrate that it is possible to completely automate the search for benchmark …
[HTML][HTML] DIGNEA: A tool to generate diverse and discriminatory instance suites for optimisation domains
To advance research in the development of optimisation algorithms, it is crucial to have
access to large test-beds of diverse and discriminatory instances from a domain that can …
access to large test-beds of diverse and discriminatory instances from a domain that can …
Towards optimisers thatKeep Learning'
We consider optimisation in the context of the need to apply an optimiser to a continual
stream of instances from one or more domains, and consider how such a system might'keep …
stream of instances from one or more domains, and consider how such a system might'keep …
Towards portfolios of streamlined constraint models: a case study with the balanced academic curriculum problem
Augmenting a base constraint model with additional constraints can strengthen the
inferences made by a solver and therefore reduce search effort. We focus on the automatic …
inferences made by a solver and therefore reduce search effort. We focus on the automatic …
Automatic streamlining for constrained optimisation
Augmenting a base constraint model with additional constraints can strengthen the
inferences made by a solver and therefore reduce search effort. We focus on the automatic …
inferences made by a solver and therefore reduce search effort. We focus on the automatic …
Streamlined constraint reasoning: an automated approach from high level constraint specifications
P Spracklen - 2022 - research-repository.st-andrews.ac …
Constraint Programming (CP) is a powerful technique for solving large-scale combinatorial
(optimisation) problems. Solving a problem proceeds in two distinct phases: modelling and …
(optimisation) problems. Solving a problem proceeds in two distinct phases: modelling and …
Exploring instance generation for automated planning
Many of the core disciplines of artificial intelligence have sets of standard benchmark
problems well known and widely used by the community when developing new algorithms …
problems well known and widely used by the community when developing new algorithms …