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In this paper, a jumping gene genetic algorithm is adopted to identify the topology of biological neural networks. The neural network is modeled with Hindmarsh-Rose neurons with synaptic coupling. Based on a single observable state of each neuron, it is possible to reveal the topology of the entire network under a framework of synchronization. The(More)
In this paper, different existing optimization algorithms for resource placement in content delivery network (CDN) are studied. It is confirmed that the best sub-optimal solution can be obtained by greedy algorithm, as compared with tabu search and direct-coded genetic algorithm. To further improve the design of CDN, a hybrid approach combining an(More)
In this paper, two newly-designed jumping gene (JG) operations are proposed for the enhancement of genetic algorithm (GA). Based on the recent findings on their schemata growth rates, it is recognized that JG can provide the essential diversity in a GA so as to greatly enhance its searching ability. This nature is particularly important for multi-objective(More)
Multi-depot vehicle routing problem (MDVRP) is well-known as a combinatorial optimization problem and it is NP-completed. Existing methods are commonly heuristics, and hence the solutions are suboptimal. In this paper, with a novel design of chromosome structure, a multiple objective genetic algorithm is proposed to tackle with this problem, such that two(More)
Inspired by the gene transposition in biological genome, recently, a new evolutionary computing algorithm has been developed for optimization. It consists of two newly designed operations: copy-and-paste and cut-and-paste, which have been proven mathematically on the basis of schema theory. In this paper, their uniqueness for balancing the exploration and(More)
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