Computing the optimal action sequence by niche genetic algorithm

Authors:
Chen Lin;Huang Jie;Gong Zheng-Hu
Affiliations:
School of Computer Science, National University of Defense Technology, Changsha, China;School of Computer Science, National University of Defense Technology, Changsha, China;School of Computer Science, National University of Defense Technology, Changsha, China
Venue:
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
Year:
2005

Citing 3
Cited 0

Printer troubleshooting using Bayesian networks

IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters

UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Decision-theoretic troubleshooting: a framework for repair and experiment

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Diagnosis aims to identify faults that explain symptoms by a sequence of actions. Computing minimum cost of diagnosis is a NP-Hard problem if diagnosis actions are dependent. Some algorithms about dependent actions are proposed but their problem descriptions are not accurate enough and the computation cost is high. A precise problem model and an algorithm named NGAOAS (Niche Genetic Algorithm for the Optimal Action Sequence) are proposed in this paper. NGAOAS can avoid running out of memory by forecasting computing space, and obtain better implicit parallelism than normal genetic algorithm, which makes the algorithm be easily computed under grid environments. When NGAOAS is executed, population is able to hold diversity and avoid premature problem. At the same time, NGAOAS is aware of adaptive degree of each niche that can reduce the computation load. Compared with updated P/C algorithm, NGAOAS can achieve lower diagnosis cost and obtain better action sequence.