國際簡稱:MEMET COMPUT 參考譯名:模因計算
主要研究方向:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-OPERATIONS RESEARCH & MANAGEMENT SCIENCE 非預警期刊 審稿周期:
《模因計算》(Memetic Computing)是一本由Springer Berlin Heidelberg出版的以COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-OPERATIONS RESEARCH & MANAGEMENT SCIENCE為研究特色的國際期刊,發表該領域相關的原創研究文章、評論文章和綜述文章,及時報道該領域相關理論、實踐和應用學科的最新發現,旨在促進該學科領域科學信息的快速交流。該期刊是一本未開放期刊,近三年沒有被列入預警名單。該期刊享有很高的科學聲譽,影響因子不斷增加,發行量也同樣高。
Memes have been defined as basic units of transferrable information that reside in the brain and are propagated across populations through the process of imitation. From an algorithmic point of view, memes have come to be regarded as building-blocks of prior knowledge, expressed in arbitrary computational representations (e.g., local search heuristics, fuzzy rules, neural models, etc.), that have been acquired through experience by a human or machine, and can be imitated (i.e., reused) across problems.
The Memetic Computing journal welcomes papers incorporating the aforementioned socio-cultural notion of memes into artificial systems, with particular emphasis on enhancing the efficacy of computational and artificial intelligence techniques for search, optimization, and machine learning through explicit prior knowledge incorporation. The goal of the journal is to thus be an outlet for high quality theoretical and applied research on hybrid, knowledge-driven computational approaches that may be characterized under any of the following categories of memetics:
Type 1: General-purpose algorithms integrated with human-crafted heuristics that capture some form of prior domain knowledge; e.g., traditional memetic algorithms hybridizing evolutionary global search with a problem-specific local search.
Type 2: Algorithms with the ability to automatically select, adapt, and reuse the most appropriate heuristics from a diverse pool of available choices; e.g., learning a mapping between global search operators and multiple local search schemes, given an optimization problem at hand.
Type 3: Algorithms that autonomously learn with experience, adaptively reusing data and/or machine learning models drawn from related problems as prior knowledge in new target tasks of interest; examples include, but are not limited to, transfer learning and optimization, multi-task learning and optimization, or any other multi-X evolutionary learning and optimization methodologies.
CiteScore | SJR | SNIP | CiteScore 指數 | ||||||||||||
6.8 | 0.945 | 1.1 |
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名詞解釋:CiteScore 是衡量期刊所發表文獻的平均受引用次數,是在 Scopus 中衡量期刊影響力的另一個指標。當年CiteScore 的計算依據是期刊最近4年(含計算年度)的被引次數除以該期刊近四年發表的文獻數。例如,2022年的 CiteScore 計算方法為:2022年的 CiteScore =2019-2022年收到的對2019-2022年發表的文件的引用數量÷2019-2022年發布的文獻數量 注:文獻類型包括:文章、評論、會議論文、書籍章節和數據論文。
Top期刊 | 綜述期刊 | 大類學科 | 小類學科 | ||
否 | 否 | 計算機科學 | 2區 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計算機:人工智能 OPERATIONS RESEARCH & MANAGEMENT SCIENCE 運籌學與管理科學 | 2區 2區 |
Top期刊 | 綜述期刊 | 大類學科 | 小類學科 | ||
否 | 否 | 計算機科學 | 3區 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計算機:人工智能 OPERATIONS RESEARCH & MANAGEMENT SCIENCE 運籌學與管理科學 | 3區 3區 |
Top期刊 | 綜述期刊 | 大類學科 | 小類學科 | ||
否 | 否 | 計算機科學 | 3區 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計算機:人工智能 OPERATIONS RESEARCH & MANAGEMENT SCIENCE 運籌學與管理科學 | 3區 3區 |
Top期刊 | 綜述期刊 | 大類學科 | 小類學科 | ||
否 | 否 | 工程技術 | 2區 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計算機:人工智能 OPERATIONS RESEARCH & MANAGEMENT SCIENCE 運籌學與管理科學 | 3區 3區 |
Top期刊 | 綜述期刊 | 大類學科 | 小類學科 | ||
否 | 否 | 計算機科學 | 3區 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計算機:人工智能 OPERATIONS RESEARCH & MANAGEMENT SCIENCE 運籌學與管理科學 | 3區 3區 |
Top期刊 | 綜述期刊 | 大類學科 | 小類學科 | ||
否 | 否 | 計算機科學 | 3區 | OPERATIONS RESEARCH & MANAGEMENT SCIENCE 運籌學與管理科學 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計算機:人工智能 | 3區 4區 |
按JIF指標學科分區 | 收錄子集 | 分區 | 排名 | 百分位 |
學科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | SCIE | Q2 | 82 / 197 |
58.6% |
學科:OPERATIONS RESEARCH & MANAGEMENT SCIENCE | SCIE | Q2 | 32 / 106 |
70.3% |
按JCI指標學科分區 | 收錄子集 | 分區 | 排名 | 百分位 |
學科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | SCIE | Q2 | 86 / 198 |
56.82% |
學科:OPERATIONS RESEARCH & MANAGEMENT SCIENCE | SCIE | Q2 | 38 / 106 |
64.62% |
Author: Feng Yao, Yiping Yao, Lining Xing, Huangke Chen, Zhongwei Lin, Tianlin Li
Journal: Memetic Computing, 2019, Vol., , DOI:10.1007/s12293-019-00284-3
Author: Xinyu Li, Shengqiang Xiao, Cuiyu Wang, Jin Yi
Journal: Memetic Computing, 2019, Vol., , DOI:10.1007/s12293-019-00283-4
Author: Xiaoxiong Zhang, Keith W. Hipel, Yuejin Tan
Journal: Memetic Computing, 2019, Vol., , DOI:10.1007/s12293-019-00282-5
Author: Jianfeng Qiu, Minghui Liu, Lei Zhang, Wei Li, Fan Cheng
Journal: Memetic Computing, 2019, Vol., , DOI:10.1007/s12293-019-00280-7
Author: Libao Deng, Lili Zhang, Haili Sun, Liyan Qiao
Journal: Memetic Computing, 2019, Vol., , DOI:10.1007/s12293-019-00279-0
Author: Xian-Bo Wang, Zhi-Xin Yang, Pak Kin Wong, Chao Deng
Journal: Memetic Computing, 2018, Vol., , DOI:10.1007/s12293-018-0277-2
Author: Xiuli Wu, Xiajing Liu, Ning Zhao
Journal: Memetic Computing, 2018, Vol., , DOI:10.1007/s12293-018-00278-7
Author: Xin Zhang, Shiu Yin Yuen
Journal: Memetic Computing, 2013, Vol.5, 187-211, DOI:10.1007/s12293-013-0117-3
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