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Found 12 papers, 4 papers with code
Revisit the Algorithm Selection Problem for TSP with Spatial Information Enhanced Graph Neural Networks
In this paper, we revisit the algorithm selection problem for TSP, and propose a novel Graph Neural Network (GNN), called GINES.
Digital Twin Applications in Urban Logistics: An Overview
In this paper, we seek to provide a framework by which DTs could be easily adapted to urban logistics networks.
Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems
The DRL policy is trained to adaptively set the values that dictate the intensity and probability of mutation for solutions during optimization.
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
The challenge is to learn them from available data, while taking into account a set of hard constraints that a solution must satisfy, and that solving the optimisation problem (esp.
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
Overall, by organizing this competition we have introduced routing problems as an interesting problem setting for AI researchers.
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.
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.
Online Optimization with Costly and Noisy Measurements using Random Fourier Expansions
This paper analyzes DONE, an online optimization algorithm that iteratively minimizes an unknown function based on costly and noisy measurements.
Exploration and Exploitation in Visuomotor Prediction of Autonomous Agents
This paper discusses various techniques to let an agent learn how to predict the effects of its own actions on its sensor data autonomously, and their usefulness to apply them to visual sensors.
Universal Approximation Using Shuffled Linear Models
This paper proposes a specific type of Local Linear Model, the Shuffled Linear Model (SLM), that can be used as a universal approximator.
