Memetic Computing
Note: The following journal information is for reference only. Please check the journal website for updated information prior to submission.
Journal Title
Memetic Computing
MEMET COMPUT
ISSN / eISSN
1865-9284 / 1865-9292
Aims and Scope
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.
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.
Subject Area
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
OPERATIONS RESEARCH & MANAGEMENT SCIENCE
CiteScore
6.70
View Trend
CiteScore Ranking
| Category | Quartile | Rank |
|---|---|---|
| Mathematics - Control and Optimization | Q1 | #12/121 |
| Mathematics - General Computer Science | Q1 | #37/233 |
Web of Science Core Collection
| Science Citation Index Expanded (SCIE) | Social Sciences Citation Index (SSCI) |
|---|---|
| Indexed | - |
| Category (Journal Citation Reports 2023) | Quartile |
|---|---|
| COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE | Q2 |
| OPERATIONS RESEARCH & MANAGEMENT SCIENCE - SCIE | Q2 |
H-index
26
Country/Area of Publication
GERMANY
Publisher
Springer Berlin Heidelberg
Publication Frequency
4 issues per year
Year Publication Started
2009
Annual Article Volume
33
Open Access
NO
Contact
TIERGARTENSTRASSE 17, HEIDELBERG, GERMANY, D-69121
Verified Reviews
April 15, 2021 - First response (Two reviewers, one pointed out some spelling mistakes, the other requested additional statistical experiments)
April 28, 2021 - Resubmitted
July 16, 2021 - Second response (The first reviewer had no further comments, the second reviewer requested more detailed experimental content and also asked for an explanation of the algorithm's advantages in the conclusion)
July 24, 2021 - Resubmitted
August 4, 2021 - Under viewer
September 4, 2021 - Rejected
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