دانلود مقاله ISI انگلیسی شماره 78841
عنوان فارسی مقاله

خوب در برابر دانش بد: الگوریتم های تکاملی هدایت شده هستی شناسی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
78841 2015 13 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Good versus bad knowledge: Ontology guided evolutionary algorithms
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 42, Issue 21, 30 November 2015, Pages 8039–8051

کلمات کلیدی
الگوریتم های تکاملی؛ دانش هدایت شده؛ الگوریتم ژنتیک؛ هستی شناسی؛ درخت های تصمیم گیری
پیش نمایش مقاله
پیش نمایش مقاله خوب در برابر دانش بد: الگوریتم های تکاملی هدایت شده هستی شناسی

چکیده انگلیسی

Good knowledge would be expected to help a knowledge-based algorithm more than bad knowledge. In this research, the precise effect of good versus bad knowledge on evolutionary algorithms is explored. The testable hypothesis of this paper is that good knowledge will have a significant effect on the evolutionary mutation process, whereas bad knowledge will have no significant effect. A knowledge-guided evolutionary algorithm is developed where ontologies, representing knowledge, are applied to the mutation process. Bad knowledge is represented as a randomly generated ontology, while good knowledge is represented by ontologies constructed with domain knowledge and following a formal ontology development process. Decision trees are evolved to solve a classification problem. Fitness is classification accuracy. The experiment is replicated over 2 data-sets from different domains with one being time-series, financial data and the other being wine data. As hypothesized, poorly constructed, or bad knowledge, has no effect while good knowledge is shown to have a significant effect. Bad knowledge, being random in character in these experiments, has understandably no impact on an already random mutation process. However, employing knowledge to guide the mutation process significantly constrains the traversal of the search space. Employing knowledge in an evolutionary algorithm has the potential to increase the efficiency and accuracy of evolutionary algorithms.

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