| 期刊全称 | Memetic Computing | ||||||||||||||||||||||||||||||||||||
| 期刊缩写 | MEMET COMPUT | ||||||||||||||||||||||||||||||||||||
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涉及主题
（科研通AI识别） |
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| 期刊介绍 | 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. | ||||||||||||||||||||||||||||||||||||
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| 期刊ISSN | print: 1865-9284on-line: 1865-9292 | ||||||||||||||||||||||||||||||||||||
| 2023最新影响因子 (2024年6月20日公布) 与上一年的差值 |
3.3↓ 1.4 | ||||||||||||||||||||||||||||||||||||
| 历年影响因子 |
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| 历年发表/被引量 （科研通AI通过大数据分析） |
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| h-index（2021） | 26 | ||||||||||||||||||||||||||||||||||||
| 自引率 | 12.10% | ||||||||||||||||||||||||||||||||||||
| 涉及的研究领域 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-OPERATIONS RESEARCH & MANAGEMENT SCIENCE | ||||||||||||||||||||||||||||||||||||
| WOS期刊分区 （2024年6月20日公布） |
JCR学科分类
JCI学科分类
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| 中科院SCI期刊分区 （2023年12月升级版） |
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| 中科院SCI期刊分区 （2022年12月升级版） |
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| 中科院SCI期刊分区 （2021年12月基础版） |
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| 中科院SCI期刊分区 （2021年12月升级版） |
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| 中科院《国际期刊预警名单（试行）》名单 |
2024年02月发布的2024版：不在预警名单中 2023年01月发布的2023版：不在预警名单中 2021年12月发布的2021版：不在预警名单中 2021年01月发布的2020版：不在预警名单中 |
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| 期刊主页 |
（系统检测到多个链接，仅供参考，如有错误，请通过页面底部反馈） |
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| 投稿网址 | https://www.editorialmanager.com/meme | ||||||||||||||||||||||||||||||||||||
| 编辑部地址 | TIERGARTENSTRASSE 17, HEIDELBERG, GERMANY, D-69121 | ||||||||||||||||||||||||||||||||||||
| 出版商 | Springer Berlin Heidelberg | ||||||||||||||||||||||||||||||||||||
| 出版国家（地区） | GERMANY | ||||||||||||||||||||||||||||||||||||
| 出版语言 | English | ||||||||||||||||||||||||||||||||||||
| 出版周期 | 4 issues per year | ||||||||||||||||||||||||||||||||||||
| 创刊年份 | 2009 | ||||||||||||||||||||||||||||||||||||
| 每年出版文章数 | 31 | ||||||||||||||||||||||||||||||||||||
| Gold OA文章占比 | 8.64% | ||||||||||||||||||||||||||||||||||||
| 原创研究文献占比 （排除综述） |
100.00% | ||||||||||||||||||||||||||||||||||||
| SCI收录类型 |
Science Citation Index Expanded (SCIE) Scopus (CiteScore) |
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| PubMed链接 | http://www.ncbi.nlm.nih.gov/nlmcatalog?term=1865-9284%5BISSN%5D | ||||||||||||||||||||||||||||||||||||
| 平均审稿周期 | 网友分享经验： |
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| 平均录用比例 | 网友分享经验： |
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| 相关链接 |
您可以在上述网站查看该期刊的网友互动，及期刊影响力的其它指标。
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