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Engineering - Computational Intelligence and Complexity | Memetic Computing - incl. option to publish open access (Editorial Board)

Memetic Computing

Memetic Computing

Editor-in-Chief: Meng-Hiot Lim

ISSN: 1865-9284 (print version)
ISSN: 1865-9292 (electronic version)

Journal no. 12293

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Meng-Hiot Lim, Nanyang Technological University, Singapore
E-mail: emhlim@ntu.edu.sg

Technical Editor-in-Chief (Founding)
Yew Soon Ong, Nanyang Technological University, Singapore

Board Members
Samad Ahmadi, De Montfort University, UK
Regina Berretta, The University of Newcastle, UK
Jiuwen Cao, Hangzhou Dianzi University, China
Carlos Coello Coello, CINVESTAV-IPN, Mexico
Carlos Cotta, Universtiy of Malaga, Spain
Maoguo Gong, Xidian University, China
Wenyin Gong, China University of Geosciences, China
Hisao Ishibuchi, Osaka Prefecture University, Japan
Wilfried Jakob, Karlsruhe Institute of Technology (KIT), Germany
Yaochu Jin, University of Surrey, UK
Dario Landa-Silva, University of Nottingham, UK
Feng Liang, Chongqing University, China
Jose Lozano, University of the Basque Country, Spain
Ami Moshaiov, Tel Aviv University, Israel
Andreas Nearchou, University of Patras, Greece
Ferrante Neri, De Monfort University, UK
Zhang Qingfu, City University of Hong Kong, Hong Kong
Ruhul Sarker, University of new South Wales, Australia
Jim Smith, University of the West of England, UK
Kay-Chen Tan, National University of Singapore, Singapore
Ke Tang, Univ of Science and Technology of China, China
Chuan-Kang Ting, National Chung Cheng University, Taiwan
Jim Torresen, University of Oslo, Norway
Ling Wang, Tsinghua University, China
Kevin Wong, Murdoch University, Australia
Annie S. Wu, University of Central Florida, USA
Zhu Zhexuan, Shenzhen University, China


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For authors and editors

  • Journal Citation Reports®
    2017 Impact Factor
  • 1.925
  • Aims and Scope

    Aims and Scope


    Memetic Computing features articles on high quality research in hybrid metaheuristics (including evolutionary hybrids) for optimization, control and design in continuous and discrete optimization domains. It goes beyond current search methodologies towards innovative research on the emergence of cultural artifacts such as game, trade and negotiation strategies and, more generally, rules of behavior as they apply to, for example, robotic, multi-agent and artificial life systems.

    Memetic Computing is an avenue for the latest results in natural computation, artificial intelligence, machine learning, operational research and natural sciences, which are combined in novel ways so as to transcend the intrinsic limitations of a single discipline.

    Potential authors are invited to submit original research articles for publication consideration at any time.  Reviews and short research communications are also welcomed. Further information on submission, format, lengths and style files is available through the journal website.  All manuscripts should be submitted electronically using the Online Submission system.

    We aim to achieve a typical turnaround time of not more than 3 months for the review process.

    Some (but not all) of the topics covered by Memetic Computing are:

    • Algorithmic Intelligence in Optimisation, Control and Design
    • Hybrid (Parallel) Metaheuristics such as Tabu Search, Path relinking, Scatter Search, GRASP methods, Iterated Local Search, Simulated annealing, Variable Neighborhood Search, Evolutionary Algorithms, Learning Classifier Systems, Memetic Algorithms, Cultural Algorithms, etc.
    • Approximate and exact algorithms for Combinatorial and Continuous Optimisation
    • Integer and Linear Programming
    • Ant Colony Computing
    • Self-organisation, Self-Assembly, Self-Generation, Self-Healing of artificial systems
    • Swarm Intelligence
    • Neural networks
    • Evolutionary Dynamics
    • Memetic Theory
    • Artificial Cultures in multi-agent systems, webbots and robots.
    • Landscape Analysis
    • Methodological aspects of experimental computing.
    • Search based Software Engineering
    • Genetic Programming
    • Constraint Optimisation
    • Representation and encoding studies
    • Real-world applications
    • Machine learning and Data Mining
    • Multiobjective optimisation
    • Artificial immune systems
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