基于知识的遗传启发式学习确定性因素

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI:10.1109/ICEC.1994.350029

Douglas B. Lynch, D. Kuncicky

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引用次数: 3

摘要

专家网络是由专家系统衍生而来的一种推理网络。专家网络的用途之一是使用神经网络学习技术来改进知识库中的确定性度量。目标导向蒙特卡罗搜索(GDMC)是一种并行随机爬坡方法，被成功地用于从数据中提炼确定性因素。本文提出了一种新的启发式算法，结合遗传算法技术提高了GDMC的性能。

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A knowledge-based genetic heuristic for learning certainty factors

An expert network is a type of inference network that is derived from an expert system. One of the uses of expert networks is to to refine measures of certainty in knowledge bases using neural network learning techniques. Goal-directed Monte Carlo search (GDMC) is a parallel stochastic hillclimbing method that is being successfully used to refine certainty factors from data. This paper presents a new heuristic for GDMC that improves its performance by incorporating genetic algorithm techniques.<>

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Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence

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