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Parallel physical optimization algorithms for allocating data to multicomputer nodes
Published 1994“…Three parallel physical optimization algorithms for allocating irregular data to multicomputer nodes are presented. …”
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Allocating data to multicomputer nodes by physical optimization algorithms for loosely synchronous computations
Published 1992“…Three optimization methods derived from natural sciences are considered for allocating data to multicomputer nodes. These are simulated annealing, genetic algorithms and neural networks. …”
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Optimization of Support Structures for Offshore Wind Turbines using Genetic Algorithm with Domain-Trimming (GADT)
Published 2017“…The powerful genetic algorithm optimization technique is augmented with an innovative “domain-trimming” modification. …”
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A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks
Published 2022Subjects: -
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Multi-Objective Co-optimization of Power and Gas under Uncertainties with P2H embedded
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6
Minimizing Deadline Misses of Mobile IoT Requests in a Hybrid Fog- Cloud Computing Environment
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7
Practical Multiple Node Failure Recovery in Distributed Storage Systems
Published 2016“…The problem is solved using genetic algorithms that search within the feasible solution space. …”
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Dynamic single node failure recovery in distributed storage systems
Published 2017“…To minimize the system repair cost, we formulate our problem using incidence matrices and solve it heuristically using genetic algorithms for all possible cases of single node failures. …”
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Optimal Operation and Planning of Power System Integrated with Reverse Osmosis Water Desalination
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10
Practical single node failure recovery using fractional repetition codes in data centers
Published 2016“…Hence, a practical solution for node failures is presented by using a self-designed genetic algorithm that searches within the feasible solution space. …”
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A novel network-based SIS framework for improved GA performance
Published 2025“…We assign fit solutions as infected, with a certain probability. Then, infected nodes can spread their genetic traits to neighboring susceptible nodes through basic genetic algorithm operations within the SIS framework and based on defined probabilities. …”
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Improving the Resilience of Smart Distribution Networks against Cyber Attacks
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13
Predicting Android Malware Using Evolution Networks
Published 2025“…We combine this model with genetic algorithms to optimize its parameters and return the best state transition probabilities, and we predict future malware accordingly. …”
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14
Recent advances on artificial intelligence and learning techniques in cognitive radio networks
Published 2015“…The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. …”
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