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

Memetic Computing

Memetic Computing

Managing Editor: Meng-Hiot Lim

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

Journal no. 12293

Springer Open Access

High quality research in hybrid metaheuristics

  • Features high quality research in hybrid metaheuristics (including evolutionary hybrids) for optimization, control and design in continuous and discrete optimization domains
  • Goes beyond current search methodologies towards innovative research on the emergence of cultural artifacts
  • Presents the latest results which are fuzzed together in novel ways in order to transcend the intrinsic limitations of a single discipline

Memetic Computing's goals are:

  • To be an outlet for high quality research in hybrid metaheuristics (including evolutionary hybrids) for optimization, control and design in continuous and discrete optimization domains. We seek to dissolve the barriers separating metaheuristics, exact and approximation algorithms research and to bring forth a renewed impetus towards the investigation and understanding of promising new hybrid algorithmic technologies.
  • To go 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.
  • Ultimately, Memetic Computing aspires to serve as a focal publication where the latest results in Natural Computation, Artificial Intelligence, Machine Learning, Operational Research and Natural Sciences (e.g. cognitive, animal and insect’s behavior, etc.) are fuzzed together in novel ways in order to transcend the intrinsic limitations of a single discipline.
  • Potential authors are invited to submit original research articles for publication consideration. Reviews and short communications may also be considered. 

    Related subjects » Applications - Artificial Intelligence - Complexity - Computational Intelligence and Complexity - Robotics - Systems Biology and Bioinformatics

    Abstracted/Indexed in 

    Science Citation Index Expanded (SciSearch), Journal Citation Reports/Science Edition, SCOPUS, INSPEC, Google Scholar, Academic OneFile, Current Contents/Engineering, Computing and Technology, DBLP, EI-Compendex, io-port.net, OCLC, SCImago, Summon by ProQuest

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    • Aims and Scope

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      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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    • Instructions for Authors

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    • Author Academy: Training for Authors
    • English Language Editing

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    • CFP Metaheuristics For Biomedical Applic...
    • CFP Evolutionary Healthcare System (pdf,...
    • CFP Semantic Search and Interaction (pd...
    • CFP Hybrid Nature Inspired Algorithm (pd...
    • CFP NICSO 2014 (pdf, 200 kB)
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