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Found 8 papers, 0 papers with code
Genetic-based Constraint Programming for Resource Constrained Job Scheduling
In the proposed algorithm, evolved programs represent variable selectors to be used in the search process of constraint programming, and their fitness is determined by the quality of solutions obtained for training instances.
A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading
Designed as the foundational architecture of the trading system, the CausalReinforceNet framework enhances the capabilities of the reinforcement learning agent through causal analysis.
Causal Feature Engineering of Price Directions of Cryptocurrencies using Dynamic Bayesian Networks
The efficacy of the proposed model in predicting cryptocurrency price directions is evaluated from two perspectives.
Modelling Determinants of Cryptocurrency Prices: A Bayesian Network Approach
The ensuing networks are used for causal reasoning and diagnosis, and the results indicate that social media (in particular Twitter data in this study) is the most significant influencing factor of the prices of altcoins.
Enhancing Constraint Programming via Supervised Learning for Job Shop Scheduling
Finally, we demonstrate that hybridising the machine learning-based variable ordering methods with traditional domain-based methods is beneficial.
Adaptive Population-based Simulated Annealing for Uncertain Resource Constrained Job Scheduling
While a number of optimisation methods have been proposed to tackle the deterministic problem, the uncertainty associated with resource availability, an inevitable challenge in mining operations, has received less attention.
A hybrid deep-learning approach for complex biochemical named entity recognition
In this approach, the MHATT mechanism aims to improve the recognition accuracy of abbreviations to efficiently deal with the problem of inconsistency in full-text labels.
Instance Space Analysis for the Car Sequencing Problem
We investigate an important research question for solving the car sequencing problem, that is, which characteristics make an instance hard to solve?
