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learning algorithm » learning algorithms (Expand Search)
level scheduling » self scheduling (Expand Search)
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data algorithm » jaya algorithm (Expand Search), deer algorithm (Expand Search)
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Scheduling and allocation in high-level synthesis using genetic algorithm
Published 1994Get full text
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Bird’s Eye View feature selection for high-dimensional data
Published 2023“…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”
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Activity-level space scheduling
Published 1992“…Activity-level space scheduling involves allocating site space over time to static and dynamic construction resources such as robots. …”
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Improvement Algorithm for Limited Space Scheduling
Published 2001“…The model characterizes resource space requirements over time and establishes a time-space relationship for each activity in the schedule, based on alternative resource levels. An example illustrates the presented algorithm that generates a feasible space schedule.…”
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Scheduling and allocation in high-level synthesis using stochastic techniques
Published 2020“…In this work, a unique approach to scheduling and allocation problem using the genetic algorithm (GA) is described. …”
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Efficient Dynamic Cost Scheduling Algorithm for Data Batch Processing
Published 2016Get full text
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Concurrent BIST Synthesis and Test Scheduling Using Genetic Algorithms
Published 2007“…This paper presents an efficient method for concurrent built-in self-test synthesis and test scheduling in high-level synthesis. The method maximizes concurrent testing of modules while performing the allocation of functional units, test registers, and interconnects. …”
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Multi-Agent Learning of Strategies in Abstract Argumentation Mechanisms
Published 2009“…The importance of this kind of work lies in the fact that it combines two aspects of multi-agent systems that have been quite separate to-date: argumentation protocols and multi-agent learning in games.…”
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Deep Reinforcement Learning for Resource Constrained HLS Scheduling
Published 2022“…The two main steps in HLS are: operations scheduling and data-path allocation. In this work, we present a resource constrained scheduling approach that minimizes latency and subject to resource constraints using a deep Q learning algorithm. …”
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masterThesis -
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Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
Published 2021“…An iterative dynamic scheduling algorithm (DCSDBP) was developed to address the data batching process. …”
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Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management
Published 2019Get full text
doctoralThesis -
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Single channel speech denoising by DDPG reinforcement learning agent
Published 2025“…In this paper, a novel SD algorithm is presented based on the deep deterministic policy gradient (DDPG) agent; an off-policy reinforcement learning (RL) agent with a continuous action space. …”
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Experimental evaluation of multi-agent reinforcement learning in real-world scale-free networks
Published 2010“…However, the study and analysis of the state-of-the-art multi-agent reinforcement learning (MARL) algorithms have been limited to small problems involving few number of learning agents.The purpose of this project is to conduct an extensive evaluation and comparison of MARL algorithms when used in networks that exhibit the scale-free property. …”
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MoveSchedule
Published 1995“…MoveSchedule establishes a time-space relationship for each activity in the schedule based on alternative resource levels so that tradeoffs between activity duration and space need on site are possible. …”
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Meta-Heuristic Procedures for the Multi-Resource Leveling Problem with Activity Splitting
Published 2011Get full text
doctoralThesis -
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Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms
Published 2025“…Given the complexity of the LDTP solution for managing online requests, we propose a real-time, lightweight solution using multi-agent meta-reinforcement learning. Our approach is tested on CNN networks and benchmarked against state-of-the-art conventional reinforcement learning algorithms. …”