Memetic Computing

雜志介紹

Memetic Computing雜志介紹

《Memetic Computing》是一本以English為主的未開放獲取國際優秀期刊，中文名稱模因計算，本刊主要出版、報道計算機科學-COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE領域的研究動態以及在該領域取得的各方面的經驗和科研成果，介紹該領域有關本專業的最新進展，探討行業發展的思路和方法，以促進學術信息交流，提高行業發展。該刊已被國際權威數據庫SCIE收錄，為該領域相關學科的發展起到了良好的推動作用，也得到了本專業人員的廣泛認可。該刊最新影響因子為3.3，最新CiteScore 指數為6.8。

本刊近期中國學者發表的論文主要有：

• An intelligent scheduling algorithm for complex manufacturing system simulation with frequent synchronizations in a cloud environment

  Author: Feng Yao, Yiping Yao, Lining Xing, Huangke Chen, Zhongwei Lin, Tianlin Li

• Mathematical modeling and a discrete artificial bee colony algorithm for the welding shop scheduling problem

  Author: Xinyu Li, Shengqiang Xiao, Cuiyu Wang, Jin Yi

• Project portfolio selection and scheduling under a fuzzy environment

  Author: Xiaoxiong Zhang, Keith W. Hipel, Yuejin Tan

• A multi-level knee point based multi-objective evolutionary algorithm for AUC maximization

  Author: Jianfeng Qiu, Minghui Liu, Lei Zhang, Wei Li, Fan Cheng

英文介紹

Memetic Computing雜志英文介紹

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.