Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
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In this paper, we present an efficient algorithm based on genetic algorithm to design an optimal backbone network topology, which incorporates some real life constraints of cost and reliability. A network topology is a 1-FT topology if it can survive a single link failure. The problem is to find a reliable network topology for a set of nodes whose total link cost is to minimize, subject to the constraints that the backbone network can tolerate a 1-link failure and total link cost of the whole network within the budget. The problem is NP-hard i.e. there exist no polynomial time algorithms to solve this problem. The proposed algorithm is a one where the optimization problem is modeled and solved by using the approach of genetic algorithm. In this algorithm we used a binary string to represent a solution point, and the time complexity of the algorithm is O (N2). As the encoding of solution points require time in the order of O (N2), hence the subsequent part of the algorithm also have the same time complexity. The algorithm is tested for a wide range of node set, and the optimizing (i.e. how a optimum solution can obtained) and timing efficiency (i.e. time require to converge the solution points to an optimal one) are studied through extensive simulation and compared with the existing optimizing algorithm (based on Genetic Algorithm) proposed by Cheng [1]. We find that our proposed algorithm can converge to an optimum much faster than the existing algorithm. And it also finds out a better search region at the iteration, we also show that our proposed algorithm could efficiently find an optimal (or sub-optimal in some cases) for most of the cases.