Journal Information

Memetic Computing

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Impact Factor:
2.3
Publisher:
Springer
ISSN:
1865-9284
Viewed:
16216
Tracked:
0

Call For Papers

Memetic Computing is an academic journal published by Springer. (ISSN 1865-9284, impact factor 2.3).

Aims and scope Many instances of research in computer science and artificial intelligence (AI) draw inspiration from metaphorical real-world physical and biological systems. Notwithstanding, cultural evolution embodies memes as basic units of transferable information within the mental faculty and propagate across populations through imitation and adaptation. Algorithmically, they are essentially knowledge building blocks expressed in various computational representations (e.g., heuristics, simple or fuzzy logic, decision trees, artificial neural models, probability distributions, etc.). Extending and generalizing the genetic metaphor for evolutionary optimization, these representations are acquired through human or machine experience and can be exploited through imitation and adaptation, significantly enhancing the effectiveness of problem-solving systems. The emergence and advancement in machine learning has transformed the scientific landscape of algorithmics and AI into a symbiotic embodiment which is consistent with the conceptual ideology of memetic computing. This has shifted the perception of multiple competing "paradigms" to the idea that an algorithm can seamlessly blend multiple heterogeneous elements within a problem-solving framework. In this context, most modern machine learning algorithms implicitly and explicitly embrace the philosophy of memetic computing. This journal publishes original research articles focusing on algorithmic aspects and innovative implementations of machine learning and AI, with an interest in both exact and heuristic approaches. Review articles offering original insights into the current state-of-art research as well as forward looking research developments are also welcome. Additionally, interesting real-world applications that perpetuate the notion of knowledge as building blocks acquired within an AI foundational framework are encouraged.
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