该算法利用问题的邻域知识指导局部搜索，可克服元启发式算法随机性引起的盲目搜索。
The proposed algorithm utilizes neighborhood knowledge to direct its local search procedure which can overcome the blindness or randomness introduced by meta-heuristics.
因此本文针对标的本身的结构提出了四种启发式信息及两种求解器：二元蚁群算法及贪婪算法。
By using the intrinsic characters of this model, we design four types of heuristic information for bid and two problem solvers: the binary ant colony algorithm and the greedy algorithm.
神经元网络启发式的并行、分布特征和可学习性为专家系统的知识表达和获取、不确定性推理提供了新的途径。
The parallelism, distribution and capability of learning of neural net heuristics provide a new way for knowledge acquisition, knowledge representation and uncertainty reasoning in expert systems.
神经元网络启发式的并行、分布特征和可学习性为专家系统的知识表达和获取、不确定性推理提供了新的途径。
The parallelism, distribution and capability of learning of neural net heuristics provide a new way for knowledge acquisition, knowledge representation and uncertainty reasoning in expert systems.
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