Memetic Computing

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.

【译文】迷因被定义为存在于大脑中的可转移信息的基本单元，通过模仿过程在人群中传播。从算法的角度来看，迷因已成为先前知识的构建块，以任意计算表示形式（例如，局部搜索启发式方法、模糊规则、神经网络模型等）表达，这些知识是通过人类或机器的经验获得的，并且可以在不同问题中模仿（即重用）。《迷因计算》期刊欢迎将上述社会文化意义上的迷因概念融入人工系统的论文，特别强调通过明确地融入先前知识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研究和应用研究的高质量平台，这些研究可能被归类为以下迷因类别：类型1：集成人类设计的启发式方法的通用算法，捕获某种形式的先前领域知识；例如，将进化全局搜索与特定问题的局部搜索相结合的传统迷因算法。类型2：能够从多样化的选择中自动选择、适应和重用最合适的启发式方法的算法；例如，在给定的优化问题中，学习全局搜索算子与多个局部搜索方案之间的映射。类型3：能够通过经验自主学习的算法，自适应地重用来自相关问题的数据和/或机器学习模型作为新目标任务中的先前知识；示例包括但不限于迁移学习与优化、多任务学习与优化，或任何其他多-X进化学习与优化方法。