基于遗传算法的关联分类器系统的实现

[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence Pub Date : 1990-11-06 DOI:10.1109/TAI.1990.130308

Kirk Twardowski

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引用次数: 10

摘要

本文介绍了基于遗传算法的ACS发展的第一个结果。ACS是将分类器系统固有的并行性映射到在基于pc的关联处理器上执行的程序的结果。给出了相干处理器中ACS的关联算法。结果表明，这种BOOLE分类器系统的关联实现可以学习和发布串行实现的结果。研究表明，使用关联处理器作为协处理器可以减少分类器系统的响应时间，特别是对于具有大量规则的分类器系统。事实上，当ACS中的规则数量增加一个数量级时，在删除DOS开销后，系统的响应时间只增加了25%。

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Implementation of a genetic algorithm based associative classifier system (ACS)

The first results from the development of a genetic algorithm-based ACS are presented. The ACS is a result of mapping the inherent parallelism in classifier systems to a program which executes on a PC-based associative processor. The associative algorithms of the ACS for the coherent processor are presented. It is demonstrated that this associative implementation of the BOOLE classifier system learns as well as results published for serial implementations. It is shown that the use of an associative processor as a co-processor can decrease classifier system response time, particularly for classifier systems with a large number of rules. In fact, when the number of rules in the ACS was increased by an order of magnitude, the response time of the system increased only 25% after DOS overhead was removed.<>

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[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence

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