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Titlebook: Optimization and Learning; 4th International Co Bernabé Dorronsoro,Lionel Amodeo,Patricia Ruiz Conference proceedings 2021 Springer Nature

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樓主: trace-mineral
31#
發(fā)表于 2025-3-26 21:22:03 | 只看該作者
32#
發(fā)表于 2025-3-27 04:49:02 | 只看該作者
A Learning-Based Iterated Local Search Algorithm for Solving the Traveling Salesman Probleme well-known NP-Hard Traveling Salesman Problem. This metaheuristic basically employs single local search and perturbation operators for finding the (near-) optimal solution. In this paper, by incorporating multiple local search and perturbation operators, we explore the use of reinforcement learnin
33#
發(fā)表于 2025-3-27 08:50:53 | 只看該作者
34#
發(fā)表于 2025-3-27 13:18:42 | 只看該作者
A Comparison of Learnheuristics Using Different Reward Functions to Solve the Set Covering Problem machine learning. The concept behind the hybridization of both worlds is called Learnheuristics which allows to improve optimization methods through machine learning techniques where the input data for learning is the data produced by the optimization methods during the search process. Among the mo
35#
發(fā)表于 2025-3-27 13:49:39 | 只看該作者
A Bayesian Optimisation Approach for?Multidimensional Knapsack Problemultidimensional knapsack problem with a large number of items and knapsack constraints, a two-level formulation is presented to take advantage of the global optimisation capability of the Bayesian optimisation approach, and the efficiency of integer programming solvers on small problems. The first l
36#
發(fā)表于 2025-3-27 20:28:02 | 只看該作者
37#
發(fā)表于 2025-3-28 00:41:53 | 只看該作者
Guiding Representation Learning in Deep Generative Models with Policy Gradientsion. Using such a representation as input to Reinforcement Learning (RL) approaches may reduce learning time, enable domain transfer or improve interpretability of the model. However, current state-of-the-art approaches that combine VAE with RL fail at learning good performing policies on certain RL
38#
發(fā)表于 2025-3-28 02:22:02 | 只看該作者
Deep Reinforcement Learning for?Dynamic Pricing of Perishable Productsic pricing of perishable products using DQN value function approximator. A model-free reinforcement learning approach is used to maximize revenue for a perishable item with fixed initial inventory and selling horizon. The demand is influenced by the price and freshness of the product. The convention
39#
發(fā)表于 2025-3-28 09:54:26 | 只看該作者
An Exploratory Analysis on a Disinformation Datasete the effects of this type of content have their impacts in the most diverse areas and generate more and more impacts within society. Automated fact-checking systems have been proposed by applying supervised machine learning techniques to assist in filtering fake news. However, two challenges are st
40#
發(fā)表于 2025-3-28 11:38:34 | 只看該作者
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