Setting The Mutation Rate: Scope And Limitations Of The 1/L Heuristic
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
k-Center problems with minimum coverage
Theoretical Computer Science
Adopting dynamic operators in a genetic algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Optimization models and methods for planning wireless mesh networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
A gateway placement algorithm in wireless mesh networks
WICON '07 Proceedings of the 3rd international conference on Wireless internet
Ad Hoc and Neighborhood Search Methods for Placement of Mesh Routers in Wireless Mesh Networks
ICDCSW '09 Proceedings of the 2009 29th IEEE International Conference on Distributed Computing Systems Workshops
Limitations of existing mutation rate heuristics and how a rank GA overcomes them
IEEE Transactions on Evolutionary Computation
Performance Evaluation of WMN Using WMN-GA System for Different Mutation Operators
NBIS '11 Proceedings of the 2011 14th International Conference on Network-Based Information Systems
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In this paper, the authors propose and implement a system based on Genetic Algorithms GAs called WMN-GA. They evaluated the performance of WMN-GA for 0.7 crossover rate and 0.3 mutation rate, exponential ranking and different distribution of clients considering size of giant component and number of covered users parameters. The simulation results show that for normal distribution the system has better performance. The authors also carried out simulations for 0.8 crossover rate and 0.2 mutation rate. The simulation results show that the setting for 0.7 crossover rate and 0.3 mutation rate offers better connectivity and user coverage.