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Rule Discovery with a Multi Objective Cultural Algorithm

机译:具有多目标文化算法的规则发现

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Cultural algorithms (CA) are evolutionary systems which utilize agent technology and which supports any evolutionary strategy like evolutionary algorithm or swarm intelligence or ant algorithms. CA uses a basic set of five knowledge sources (KS's) which are used in various animal species to guide the search toward best solutions and thus are better than evolutionary algorithms which are memory less blind search methods. The preserved knowledge in CA is disseminated throughout the system in future generations. Cultural algorithms have been used effectively in solving optimization problems, in engineering rule based systems, and combined with data mining to study complex social systems. However application of cultural algorithm for multi objective optimization of classification rules is hardly found in the literature. Research gap exists in using Cultural Algorithm for rule mining taking the various properties of rules as objectives for optimization. In the current study a cultural algorithm framework is proposed for rule mining considering it as a multi objective optimization problem.
机译:文化算法(CA)是利用代理技术的进化系统,其支持任何进化策略,如进化算法或群智能或蚂蚁算法。 CA采用基本集五种知识来源(KS),这些来源(ks的)用于各种动物物种,以指导对最佳解决方案的搜索,从而优于进化算法,这些算法是存储器较少的盲目搜索方法。 CA中保存的知识在整个世代的整个系统中传播。在求解优化问题,基于工程规则的系统中的优化问题以及与数据挖掘结合学习复杂社会系统的文化算法。然而,在文献中几乎没有发现对分类规则的多目标优化的文化算法。利用规则挖掘的文化算法存在研究缺口,以规则的各种属性作为优化的目标。在目前研究中,提出了将文化算法框架考虑到多目标优化问题。

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