Liu Jing

Publications (3)0 Total impact

• Conference Proceeding: Global numerical optimization using multi-agent genetic algorithm

  Zhong Weicai, Liu Jing, Xue Mingzhi, Jiao Licheng
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  ABSTRACT: A new algorithm, Multi-Agent Genetic Algorithm (MAGA), is proposed. It realizes the complex global numerical optimization via agent-agent interactions. All agents are fixed on a lattice, and they will compete or cooperate with their neighbors to increase their own energy. On the other hand, agents can also increase their energy with knowledge. In experiments, 4 multimodal benchmark functions are used to explore the effect of problem of problem dimension on the performance of MAGA. The results on functions with 20∼10,000 dimensions show that MAGA obtains good performance in solving high dimensional functions. Even when dimension is as high as 10,000, MAGA can still find high quality solutions with very low computational cost.
  Computational Intelligence and Multimedia Applications, 2003. ICCIMA 2003. Proceedings. Fifth International Conference on; 10/2003
• Conference Proceeding: Numerical optimization using organizational evolutionary algorithm

  Liu Jing, Zhong Weicai, Liu Fang, Jiao Licheng
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  ABSTRACT: In this paper, a new algorithm, organizational evolutionary algorithm (OEA), is proposed to deal with both unconstrained and constrained optimization problems. In OEA, the evolutionary operations do not act on individuals directly, but on organization. Therefore, three evolutionary operators are designed for organizations. In experiments, OEA is tested on 3 unconstrained and 6 constrained benchmark problems, and compared with three recent algorithms. The results show that OEA outperforms the three other algorithms both in the quality of solution and the computational cost.
  Computational Intelligence and Multimedia Applications, 2003. ICCIMA 2003. Proceedings. Fifth International Conference on; 10/2003
• Conference Proceeding: OCEC: a novel classification algorithm based on evolutionary computation

  Liu Jing, Zhong Weicai, Liu Fang, Jiao Licheng
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  ABSTRACT: A novel classification algorithm, OCEC (Organizational CoEvolutionary algorithm for Classification), based on evolutionary computation for data mining is proposed. It is compared to GA-based and non GA-based algorithms on 8 datasets from the UCI machine learning repository. Results show OCEC can achieve higher prediction accuracy, smaller number of rules and more stable performance.
  Signal Processing, 2002 6th International Conference on; 09/2002