| 引用本文: | 张光铎,王正志.进化计算在机器学习中的应用研究.[J].国防科技大学学报,1998,20(6):23-27.[点击复制] |
| Zhang Guangduo,Wang Zhengzhi.The Researches of Application Techniques on Machine Learning with Evolutionary Computation[J].Journal of National University of Defense Technology,1998,20(6):23-27[点击复制] |
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| 进化计算在机器学习中的应用研究 |
| 张光铎, 王正志 |
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(国防科技大学 自动控制系 湖南 长沙 410073)
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| 摘要: |
| 采用进化计算思想研制机器学习实际系统, 利用隐含并行机制及并行规则触发策略实现多目标优化技术。针对航天员模拟训练评分标准具体任务, 采取灵活的多层次动态编码方案, 建立多种简洁且完备的进化操作。利用信度分配组桶策略实现竞争机制, 依赖进化算法搜索、发现并选择适当规则, 引入启发知识产生缺省规则层次以实现多种隐式目标。设计并实现了遗传进化机器学习系统GEML-1, 该系统具有良好的鲁棒性和柔顺性。本文的遗传进化机器学习方法, 可推广应用于各种军用专家系统和军事决策支持系统的研制, 从而为人工智能在军事上的应用提供新的设计方法和实现途径。 |
| 关键词: 进化计算, 遗传机器学习, 多目标优化 |
| DOI： |
| 投稿日期：1998-03-30 |
| 基金项目:国防预研基金项目资助 |
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| The Researches of Application Techniques on Machine Learning with Evolutionary Computation |
| Zhang Guangduo, Wang Zhengzhi |
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(Department of Automatic Control, NUDT, Changsha, 410073)
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| Abstract: |
| This paper researches into the actual machine learning system taking advantage of the idea of evolutionary computation, and realizes the multi-goal optimization technique by applying the implicit parallelism mechanism and the parallel rule triggering strategy. In accordance with the specific task of machine learning, simulated pilot training grading standards, this paper adopts a kind of free hybrid coding scheme (multi-level and dynamic), establishes several simple and complete evolutionary operations, realizes the mechanism of competition by the credit assignment with brigade bucket algorithm. It relies on evolutionary algorithm to search for, find and select the proper rules, realizes several implicit objectives by inducting heuristic knowledge to create default rule structure, and designs a genetic evolutionary machine learning system (GEML-1) with satisfied robustness and flexibility. The method of machine learning can be generalized to apply in different kinds of expert system and military decision support system so as to provide a new kind of designing method and realizing path for machine learning. |
| Keywords: evolutionary computation, genetic evolutionary machine learning, multi-goal optimization |
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