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- Rong Long Wang, Kozo Okazaki
- Soft Comput.
- 2007

The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in theGA there is no rule of thumb to design theGAoperators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of… (More)

- Jiahai Wang, Yiqiao Cai, Yalan Zhou, Rong Long Wang, Caiwei Li
- Computers & Industrial Engineering
- 2011

- Yong Li, Zheng Tang, Guangpu Xia, Rong Long Wang
- IEEE Trans. on Circuits and Systems
- 2005

- Zhi-Qiang Chen, Rong Long Wang
- ICNC
- 2010

This paper presents an efficient generation alternation model for real-coded genetic algorithm called rc-CGA. The most important characteristic of the proposed rcCGA 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)

- Zhi-Qiang Chen, Rong Long Wang
- IEICE Transactions
- 2011

- Rong Long Wang, Kozo Okazaki
- IEICE Transactions
- 2006

An improved genetic algorithm for solving the graph planarization 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 algorithms. The… (More)

- Rong Long Wang, Kozo Okazaki
- Neurocomputing
- 2005

- Rong Long Wang, Zheng Tang, Qi Ping Cao
- Neural Computation
- 2003

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)

- Rong Long Wang, Zheng Tang, Qi Ping Cao
- Neurocomputing
- 2002

In this letter, we utilize the Hop!eld network learning method to adjust the balance between constraint term and cost term of the energy function so that the local minimum that the network once falls into vanishes and the network can continue updating in a gradient descent direction of energy. We applied the proposed learning method to the traveling… (More)

- Jiahai Wang, Zheng Tang, Rong Long Wang
- Neurocomputing
- 2004

In this paper, by adding a nonlinear self-feedback to the maximum neural network, we propose a parallel algorithm for the maximum clique problem that introduces richer and more 0exible dynamics and can prevent the network from getting stuck at local minima. A large number of instances have been simulated to verify the proposed algorithm. c © 2004 Elsevier… (More)