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Found 8 papers, 3 papers with code
Time to Stop and Think: What kind of research do we want to do?
Even if we limit the focus of this manuscript to the experimental part of the research, our main goal is to sew the seed of sincere critical assessment of our work, sparking a reflection process both at the individual and the community level.
Doubly Stochastic Matrix Models for Estimation of Distribution Algorithms
Specifically, we adopt the framework of estimation of distribution algorithms and compare DSMs to some existing proposals for permutation problems.
Neural Improvement Heuristics for Graph Combinatorial Optimization Problems
Conducted experiments demonstrate that the proposed model can recommend neighborhood operations that outperform conventional versions for the Preference Ranking Problem with a performance in the 99th percentile.
Neural Combinatorial Optimization: a New Player in the Field
Neural Combinatorial Optimization attempts to learn good heuristics for solving a set of problems using Neural Network models and Reinforcement Learning.
Comparing Two Samples Through Stochastic Dominance: A Graphical Approach
Non-deterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples in which unpredictable outcomes are common.
Constrained Combinatorial Optimization with Reinforcement Learning
This paper presents a framework to tackle constrained combinatorial optimization problems using deep Reinforcement Learning (RL).
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments.
Evolving Neural Networks in Reinforcement Learning by means of UMDAc
Neural networks are gaining popularity in the reinforcement learning field due to the vast number of successfully solved complex benchmark problems.
