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Found 8 papers, 4 papers with code
Using Automated Algorithm Configuration for Parameter Control
Dynamic Algorithm Configuration (DAC) tackles the question of how to automatically learn policies to control parameters of algorithms in a data-driven fashion.
A portfolio-based analysis method for competition results
Competitions such as the MiniZinc Challenges or the SAT competitions have been very useful sources for comparing performance of different solving approaches and for advancing the state-of-the-arts of the fields.
A Framework for Generating Informative Benchmark Instances
This separation presents an opportunity for automated approaches to generate instance data that define instances that are graded (solvable at a certain difficulty level for a solver) or can discriminate between two solving approaches.
Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration
We extend this benchmark by analyzing optimal control policies that can select the parameters only from a given portfolio of possible values.
Efficient Incremental Modelling and Solving
The contribution of this work is to enable a native interaction between SAT solvers and the automated modelling system Savile Row to support efficient incremental modelling and solving.
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.
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.
Hyper-Parameter Tuning for the (1+(λ,λ)) GA
It is known that the $(1+(\lambda,\lambda))$~Genetic Algorithm (GA) with self-adjusting parameter choices achieves a linear expected optimization time on OneMax if its hyper-parameters are suitably chosen.
