Memetic Computing

Journal Title：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.

模因被定義為可轉移信息的基本單位，存在于大腦中，并通過模仿過程在人群中傳播。從算法的角度來看，模因已被視為先驗知識的構建塊，以任意計算表示形式（例如，局部搜索啟發式、模糊規則、神經模型等）表示，這些先驗知識是通過人類或機器的經驗獲得的，并且可以在問題中模仿（即重復使用）。

《模因計算》雜志歡迎將上述社會文化模因概念納入人工系統的論文，特別強調通過明確的先驗知識整合來提高計算和人工智能技術在搜索、優化和機器學習方面的有效性。因此，該期刊的目標是成為高質量理論和應用研究的出口，研究混合的、知識驅動的計算方法，這些方法可以歸為以下任何一種模因學類別：

類型 1：通用算法與人為設計的啟發式方法相結合，可以捕獲某種形式的先驗領域知識；例如，將進化全局搜索與特定于問題的局部搜索相結合的傳統模因算法。

類型 2：能夠從各種可用選項中自動選擇、調整和重用最合適啟發式方法的算法；例如，在給定優化問題的情況下，學習全局搜索運算符和多個局部搜索方案之間的映射。

類型 3：通過經驗自主學習的算法，自適應地重用從相關問題中提取的數據和/或機器學習模型作為新目標任務中的先驗知識；示例包括但不限于遷移學習和優化、多任務學習和優化、或任何其他多X進化學習和優化方法。

Memetic Computing創刊于2009年，由Springer Berlin Heidelberg出版商出版，收稿方向涵蓋COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - OPERATIONS RESEARCH & MANAGEMENT SCIENCE全領域，此刊是該細分領域中屬于非常不錯的SCI期刊，在行業細分領域中學術影響力較大，專業度認可很高，所以對原創文章要求創新性較高，如果您的文章質量很高，可以嘗試。平均審稿速度 ，影響因子指數3.3，該期刊近期沒有被列入國際期刊預警名單，廣大學者值得一試。