Memetic Computing

杂志介绍

Memetic Computing杂志介绍

《Memetic Computing》是一本以English为主的未开放获取国际优秀期刊，中文名称模因计算，本刊主要出版、报道计算机科学-COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE领域的研究动态以及在该领域取得的各方面的经验和科研成果，介绍该领域有关本专业的最新进展，探讨行业发展的思路和方法，以促进学术信息交流，提高行业发展。该刊已被国际权威数据库SCIE收录，为该领域相关学科的发展起到了良好的推动作用，也得到了本专业人员的广泛认可。该刊最新影响因子为3.3，最新CiteScore 指数为6.8。

本刊近期中国学者发表的论文主要有：

• 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

英文介绍

Memetic Computing杂志英文介绍

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.