Memetic Computing

The goal of the journal is to 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 learning and optimization methodologies.

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.