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.
    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 demonstrably novel real-world applications of memetic computing shall also be considered for publication.

    Features high quality research in hybrid metaheuristics (including evolutionary hybrids) for optimization, control and design in continuous and discrete optimization domains
    Goes beyond current search methodologies towards innovative research on the emergence of cultural artifacts
    Presents the latest results which are fuzzed together in novel ways in order to transcend the intrinsic limitations of a single discipline