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SUMMARY An improved genetic algorithm for solving the graph pla-narization problem is presented. The improved genetic algorithm which is designed to embed a graph on a plane, performs crossover and mutation conditionally instead of probability. The improved genetic algorithm is verified by a large number of simulation runs and compared with other(More)
We present a gradient ascent learning method of the Hopfield neural network for bipartite subgraph problem. The method is intended to provide a near-optimum parallel algorithm for solving the bipartite subgraph problem. To do this we use the Hopfield neural network to get a near-maximum bipartite subgraph, and increase the energy by modifying weights in a(More)
In this article, we present a solution to the maximum clique problem using a gradient-ascent learning algorithm of the Hopfield neural network. This method provides a near-optimum parallel algorithm for finding a maximum clique. To do this, we use the Hopfield neural network to generate a near-maximum clique and then modify weights in a gradient-ascent(More)
SUMMARY Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to optimization problems. However, in the ACO algorithms it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this work, we propose an(More)
This paper presents an efficient generation alternation model for real-coded genetic algorithm called rc-CGA. The most important characteristic of the proposed rc-CGA model is the implicit self-adaptive feature in its crossover and mutation mechanism. By applying two crossover operators (BLX-α and UNDX crossover) in conjunction with Non-Uniform mutation to(More)