Telecommunications network design algorithms
Telecommunications network design algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A new method for transferring CAN messages using wireless ATM
Journal of Network and Computer Applications
Network partition for switched industrial Ethernet using genetic algorithm
Engineering Applications of Artificial Intelligence
Building an interconnection between PROFIBUS and ATM networks
Journal of Network and Computer Applications
On Replacement Strategies in Steady State Evolutionary Algorithms
Evolutionary Computation
An architecture for flexible scheduling in Profibus networks
Computer Standards & Interfaces
Performance analysis of Ethernet Powerlink networks for distributed control and automation systems
Computer Standards & Interfaces
Performance analysis of PROFINET networks
Computer Standards & Interfaces
Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems
Computers and Operations Research
A hybrid Hopfield network-genetic algorithm approach for the terminal assignment problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic algorithms for delays evaluation in networked automation systems
Engineering Applications of Artificial Intelligence
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This paper presents a novel genetic algorithm to solve the industrial Ethernet network partition problem (IENPP). A new switch-device encoding is presented for the problem, and incorporated into the genetic algorithm. This encoding has several advantages against the traditional representation used in previous approaches, which will be detailed in the paper. Also, several new genetic operators included in the genetic algorithm are described in the paper. Simulations in different network partition instances have shown the good performance of our approach: it obtains better results than a previous genetic algorithm due to the incorporation of the new representation and novel operators. Also the computational time of the proposed algorithm is better than that of the existing genetic algorithm for this problem.