Memetic Computing
Memetic Computing
期刊ISSN: 1865-9284
E-ISSN: 1865-9292
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自引率: 8.7%
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COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Q3
OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Q2
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Q3
OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Q2
《新锐期刊分区表》(2026年3月发布)
大类学科
计算机科学
3区
小类学科
计算机:人工智能
3区
运筹学与管理科学
3区
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最新中科院SCI期刊分区(2025年3月升级版)
大类学科
计算机科学
3区
小类学科
计算机:人工智能
3区
运筹学与管理科学
3区
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期刊简介
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.
出版信息
出版商
Springer Berlin Heidelberg
涉及的研究方向
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-OPERATIONS RESEARCH & MANAGEMENT SCIENCE
刊期
4 issues per year
年文章数
29
出版国家或地区
GERMANY
是否OA
Cite Score(2025年最新版)
Cite Score SJR SNIP 排名
6.7 0.778 0.959
学科
大类学科:Mathematics
小类学科:Control and Optimization
分区
Q1
学科
大类学科:Mathematics
小类学科:General Computer Science
分区
Q1
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