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Found 11 papers, 6 papers with code
A Penalty-Based Guardrail Algorithm for Non-Decreasing Optimization with Inequality Constraints
This algorithm adapts a standard penalty-based method by dynamically updating the right-hand side of the constraints with a guardrail variable which adds a margin to prevent violations.
To the Max: Reinventing Reward in Reinforcement Learning
In reinforcement learning (RL), different reward functions can define the same optimal policy but result in drastically different learning performance.
Learning From Scenarios for Stochastic Repairable Scheduling
We are interested in a stochastic scheduling problem, in which processing times are uncertain, which brings uncertain values in the constraints, and thus repair of an initial schedule may be needed.
You Shall Pass: Dealing with the Zero-Gradient Problem in Predict and Optimize for Convex Optimization
The key challenge to train such models is the computation of the Jacobian of the solution of the optimization problem with respect to its parameters.
EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions
Surrogate algorithms such as Bayesian optimisation are especially designed for black-box optimisation problems with expensive objectives, such as hyperparameter tuning or simulation-based optimisation.
ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility
We present ReproducedPapers. org: an open online repository for teaching and structuring machine learning reproducibility.
Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems
One method to solve expensive black-box optimization problems is to use a surrogate model that approximates the objective based on previous observed evaluations.
Black-box Mixed-Variable Optimisation using a Surrogate Model that Satisfies Integer Constraints
A challenging problem in both engineering and computer science is that of minimising a function for which we have no mathematical formulation available, that is expensive to evaluate, and that contains continuous and integer variables, for example in automatic algorithm configuration.
Black-box Combinatorial Optimization using Models with Integer-valued Minima
When a black-box optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution.
Order Acceptance and Scheduling with Sequence-dependent Setup Times: a New Memetic Algorithm and Benchmark of the State of the Art
Sparrow integrates the exploration ability of BRKGA and the exploitation ability of ALNS.
Complexity of Scheduling Charging in the Smart Grid
In the smart grid, the intent is to use flexibility in demand, both to balance demand and supply as well as to resolve potential congestion.
