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. The journal welcomes investigations into various modes of meme transmission. Demonstrations of memetics in the context of deep neuroevolution, synergizing evolutionary search of neural architectures with lifetime learning of specific tasks or sets of tasks, are of significant interest.
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 l earning and optimization methodologies.
Potential authors are encouraged to submit original research articles, including reviews and short communications, expanding the conceptual scope of memetics (e.g., to Type-X and beyond) and/or advancing the algorithmic state-of-the-art. Articles reporting novel real-world applications of memetics in areas including, but not limited to, multi-X evolutionary computation, neuroevolution, embodied cognition and intelligence of autonomous agents, continuous and discrete optimization, knowledge-guided machine learning, computationally expensive search problems, shall be considered for publication. All submissions must include a short (up to 300 words) “Note to practitioners”, succinctly describing the type of prior knowledge incorporation proposed in the paper.

Editor-in-Chief
Meng-Hiot Lim, Nanyang Technological University, Singapore
E-mail: emhlim@ntu.edu.sg
Technical Editor-in-Chief (Founding)
Yew Soon Ong, Nanyang Technological University, Singapore

Board Members
Samad Ahmadi, De Montfort University, UK
Regina Berretta, The University of Newcastle, UK
Jiuwen Cao, Hangzhou Dianzi University, China
Carlos Coello Coello, CINVESTAV-IPN, Mexico
Carlos Cotta, Universtiy of Malaga, Spain
Maoguo Gong, Xidian University, China
Wenyin Gong, China University of Geosciences, China
Hisao Ishibuchi, Osaka Prefecture University, Japan
Wilfried Jakob, Karlsruhe Institute of Technology (KIT), Germany
Yaochu Jin, University of Surrey, UK
Dario Landa-Silva, University of Nottingham, UK
Feng Liang, Chongqing University, China
Jose Lozano, University of the Basque Country, Spain
Ami Moshaiov, Tel Aviv University, Israel
Andreas Nearchou, University of Patras, Greece
Ferrante Neri, De Monfort University, UK
Zhang Qingfu, City University of Hong Kong, Hong Kong
Ruhul Sarker, University of new South Wales, Australia
Jim Smith, University of the West of England, UK
Kay-Chen Tan, National University of Singapore, Singapore
Ke Tang, Univ of Science and Technology of China, China
Chuan-Kang Ting, National Chung Cheng University, Taiwan
Jim Torresen, University of Oslo, Norway
Ling Wang, Tsinghua University, China
Kevin Wong, Murdoch University, Australia
Annie S. Wu, University of Central Florida, USA
Zhu Zhexuan, Shenzhen University, China.