Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Combinatorics of Network Reliability
The Combinatorics of Network Reliability
A Genetic Algorithm for the Reliability Optimization of a Distributed System
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
A Simulation Algorithm for Source Terminal Communication Network Reliability
SS '96 Proceedings of the 29th Annual Simulation Symposium (SS '96)
Local search genetic algorithm for optimal design of reliablenetworks
IEEE Transactions on Evolutionary Computation
A genetic algorithm for designing distributed computer networktopologies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Recent Advances in Optimal Reliability Allocation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Performance evaluation of bandwidth allocation in ATM networks
International Journal of Business Information Systems
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This paper introduces the development and implementation of a new methodology for optimizing reliability measures of a computer communication network within specified constraints. A genetic algorithm approach with specialized encoding, crossover, and mutation operators to design a layout topology optimizing source-terminal computer communication network reliability is presented. In this work, we apply crossover at the gene level in conjunction with the regular chromosome-level crossover operators that are usually applied on chromosomes or at boundaries of nodes. This approach provides us with a much better population mixture, and hence faster convergence and better reliability. Applying regular crossover and mutation operators on the population may generate infeasible chromosomes representing a network connection. This complicates fitness and cost calculations, since reliability and cost can only be calculated on links that actually exist. In this paper, a special crossover and mutation operator is applied in a way that will always ensure production of a feasible connected network topology. This results in a simplification of fitness calculations and produces a better population mixture that gives higher reliability rates at shorter convergence times.