Memetic Computing

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.

中文简介Magazine introduction

模因被定义为存在于大脑中并通过模仿过程在人群中传播的可转移信息的基本单位。从算法的角度来看，模因已被视为先验知识的构建块，以任意计算表示（例如，局部搜索启发式、模糊规则、神经模型等）表示，这些表示是通过以下人员的经验获得的人或机器，并且可以在问题中被模仿（即重复使用）。

《模因计算》杂志欢迎将上述模因的社会文化概念纳入人工系统的论文，特别强调通过明确的先验知识整合来提高计算和人工智能技术在搜索、优化和机器学习方面的功效。因此，该期刊的目标是成为对混合、知识驱动的计算方法进行高质量理论和应用研究的渠道，这些方法可以归为以下任何类别的模因：

类型 1：通用算法与人工启发式算法相结合，捕获某种形式的先验领域知识；例如，传统的模因算法将进化全局搜索与特定问题的局部搜索混合在一起。

类型 2：能够从各种可用选择池中自动选择、调整和重用最合适的启发式算法的算法；例如，在给定优化问题的情况下，学习全局搜索运算符和多个局部搜索方案之间的映射。

类型 3：根据经验自主学习的算法，自适应地重用从相关问题中提取的数据和/或机器学习模型，作为感兴趣的新目标任务的先验知识；示例包括但不限于迁移学习和优化、多任务学习和优化，或任何其他多 X 进化学习和优化方法。

期刊简述Magazine introduction

Memetic Computing创刊于2009年，由SPRINGER HEIDELBERG出版商出版，收稿方向涵盖COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - OPERATIONS RESEARCH & MANAGEMENT SCIENCE全领域，此刊是中等级别的SCI期刊，所以过审相对来讲不是特别难，但是该刊专业认可度不错，仍然是一本值得选择的SCI期刊。影响因子指数4.700��该期刊近期没有被列入国际期刊预警名单，广大学者值得一试。

中科院分区Magazine introduction

大类学科	小类学科	分区	Top期刊	综述期刊
计算机科学	OPERATIONS RESEARCH & MANAGEMENT SCIENCE 运筹学与管理科学 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机：人工智能	3区	是	是

名词解释：
中科院分区也叫中科院JCR分区，基础版分为13个大类学科，然后按照各类期刊影响因子分别将每个类别分为四个区，影响因子5%为1区，6%-20%为2区，21%-50%为3区，其余为4区。

JCR分区Magazine introduction

大类学科	小类学科	分区
计算机科学	OPERATIONS RESEARCH & MANAGEMENT SCIENCE 运筹学与管理科学 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机：人工智能	3区

名词解释：
JCR没有设置大类，只分为176个具体学科，按当期（1年）的影响因子进行分区；JCR是按照“平均主义”思想，根据刊物IF的高至低平均划分4个区，每个区含有该领域总量25%的期刊。中科院的分区如同社会阶层的金字塔结构，1区只有5%的顶级期刊， 2~4区期刊数量也逐层增加,所以中科院的1区和2区杂志很少，杂志质量相对也高，基本都是本领域的顶级期刊。

期刊名称	领域	中科院分区	影响因子
Cryptography And Communications-discrete-structures Boolean Functions And Sequen	计算机科学	3区	1.291
User Modeling And User-adapted Interaction	计算机科学	3区	3.600
Journal On Multimodal User Interfaces	计算机科学	3区	2.900
Formal Aspects Of Computing	计算机科学	3区	1.000
International Journal Of High Performance Computing Applications	计算机科学	3区	3.100
International Journal Of High Performance Computing Applications	计算机科学	3区	3.100