Memetic Computing

Aims & 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.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.3
2024	3.30

SJR (SCImago Journal Rank)

Year	Value
2024	0.778

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	41

Journal Rank

Year	Value
2024	7039

Journal Citation Indicator

Year	Value
2024	299

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems

  Citation: 664

  Authors: Gai-Ge

• Spider Monkey Optimization algorithm for numerical optimization

  Citation: 497

  Authors: Jagdish Chand, Harish, Shimpi Singh, Maurice

• Chemical Reaction Optimization: a tutorial

  Citation: 211

  Authors: Albert Y. S., Victor O. K.

• Genetic programming for feature construction and selection in classification on high-dimensional data

  Citation: 170

  Authors: Binh, Bing, Mengjie

• A predictive gradient strategy for multiobjective evolutionary algorithms in a fast changing environment

  Citation: 150

  Authors: Wee Tat, Chi Keong, Kay Chen

• Scale factor local search in differential evolution

  Citation: 150

  Authors: Ferrante, Ville

• Hybrid multi-objective cuckoo search with dynamical local search

  Citation: 148

  Authors: Maoqing, Hui, Zhihua, Jinjun

• CBSO: a memetic brain storm optimization with chaotic local search

  Citation: 140

  Authors: Yang, Shangce, Shi, Yirui, Shuangyu, Fenggang

• Memetic algorithms for solving job-shop scheduling problems

  Citation: 119

  Authors: S. M. Kamrul, Ruhul, Daryl, David

• A Hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function

  Citation: 107

  Authors: Mohamed A., Ahmed F.

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