Memetic Computing - Springer - Impact Factor

Memetic Computing - Springer | 2024 Impact Factor:2.3 | Cite Score:6.7 | Q1

Impact Factor and Journal Rank of Memetic Computing

About: Memetic Computing is an interdisciplinary journal published by Springer that explores the synergy between different computational methodologies inspired by natural and cultural processes. It provides a platform for researchers and practitioners to present innovative research and findings in the fields of evolutionary computation, artificial intelligence, machine learning, and other areas related to memetic algorithms and hybrid systems.
Objective:
The primary objective of Memetic Computing is to advance the understanding and development of memetic algorithms and hybrid systems that combine evolutionary processes with individual learning and cultural transmission. The journal aims to promote the development and application of these techniques to solve complex optimization and search problems, thereby contributing to technological advancements in various fields.
Peer Review Process:
Memetic Computing employs a rigorous peer-review process to ensure the publication of high-quality research. Manuscripts submitted to the journal are evaluated by experts who assess the originality, technical accuracy, and significance of the research, ensuring that only the most impactful and scientifically sound papers are published.
Innovation and Impact:
The journal is dedicated to publishing groundbreaking research that pushes the boundaries of memetic algorithms and hybrid computational methods. By fostering innovation and disseminating new findings, Memetic Computing aims to influence future research directions and practical applications in computational intelligence and optimization.
Global Reach and Accessibility:
Memetic Computing attracts a global readership and author base, facilitating the exchange of ideas and advancements across different regions and disciplines. The journal is committed to making its content accessible to researchers, educators, and professionals worldwide, promoting a collaborative and inclusive scientific community.
Interdisciplinary Collaboration:
Recognizing the importance of interdisciplinary research, Memetic Computing welcomes contributions that bridge gaps between evolutionary computation, artificial intelligence, machine learning, and other related fields. This approach enriches the research landscape and encourages the development of comprehensive solutions to complex scientific challenges.
Practical Application and Management:
In addition to theoretical research, the journal publishes articles that focus on practical applications and case studies. These contributions provide valuable insights into the real-world implementation of memetic algorithms and hybrid systems, offering guidance and best practices to practitioners in the field.

Journal Home: Journal Homepage

Editor-in-Chief: Chuan-Kang Ting

Scope: Memetic Computing is an interdisciplinary journal that focuses on the theoretical, experimental, and practical aspects of memetic algorithms and other nature-inspired computing methodologies. The journal covers a broad range of topics related to the development, analysis, and application of memetic algorithms, which combine global and local search strategies to solve complex optimization problems. Areas of interest include, but are not limited to:
Memetic Algorithms:
Design, development, and analysis of memetic algorithms and their components.
Optimization Techniques:
Studies on hybrid optimization techniques that integrate memetic algorithms with other optimization methods such as genetic algorithms, evolutionary strategies, and particle swarm optimization.
Nature-Inspired Computing:
Exploration of new nature-inspired computing paradigms, including bio-inspired and culturally-inspired approaches.
Local Search Methods:
Development and application of local search methods and their integration with global search techniques.
Real-World Applications:
Application of memetic algorithms to solve real-world problems in various domains, such as engineering design, logistics, healthcare, finance, and data mining.
Theoretical Foundations:
Theoretical studies on the performance, convergence, and complexity of memetic algorithms.
Benchmarking and Performance Evaluation:
Benchmarking studies and performance evaluations of memetic algorithms compared to other optimization techniques.