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.
- 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
- Chuan-Kang Ting
- Publishing model
- Hybrid (Transformative Journal). Learn about publishing Open Access with us
- 3.860 (2019)
- Impact factor
- 3.333 (2019)
- Five year impact factor
- 76 days
- Submission to first decision
- 297 days
- Submission to acceptance
- 18,264 (2020)
Integrated scheduling problem for earth observation satellites based on three modeling frameworks: an adaptive bi-objective memetic algorithm
Authors (first, second and last of 4)
An effective memetic algorithm for UAV routing and orientation under uncertain navigation environments
Authors (first, second and last of 4)
Call for Papers: Advances in Analysis and Application of Multi-Objective Memetic Optimization Algorithms
A Special Issue open to submissions until December 31 2021.
A Special Issue open to submissions until October 1 2021.
A Special Issue open to submissions until July 15 2021.
About this journal
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