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.

模因被定义为可转移信息的基本单位，存在于大脑中，并通过模仿过程在人群中传播。从算法的角度来看，模因已被视为先验知识的构建块，以任意计算表示形式（例如，局部搜索启发式、模糊规则、神经模型等）表示，这些先验知识是通过人类或机器的经验获得的，并且可以在问题中模仿（即重复使用）。

《模因计算》杂志欢迎将上述社会文化模因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有效性。因此，该期刊的目标是成为高质量理论和应用研究的出口，研究混合的、知识驱动的计算方法，这些方法可以归为以下任何一种模因学类别：

类型 1：通用算法与人为设计的启发式方法相结合，可以捕获某种形式的先验领域知识；例如，将进化全局搜索与特定于问题的局部搜索相结合的传统模因算法。

类型 2：能够从各种可用选项中自动选择、调整和重用最合适启发式方法的算法；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：通过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新目标任务中的先验知识；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。

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