Memetic Computing Society
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Extreme Learning Machines (ELM) provide efficient unified solutions to generalized feedforward networks including but not limited to (both single‐hidden‐layer and multi‐ hidden‐layer) feedforward neural networks, RBF networks, and kernel learning. ELM possesses unique features to deal with regression and (multi‐class) classification tasks. Consequently, ELM offers significant advantages such as fast learning speed, ease of implementation, and minimal human intervention. ELM has good potential as a viable alternative technique for large‐scale computing and artificial intelligence.

Organized by Tsinghua University, Northeastern University and Nanyang Technological University, ELM2013 will be held in Beijing, the capital of China. This conference will provide a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique, as well as participating in a competition on a data‐centric application problem. Details of ELM2013 competition will be announced by March 15, 2013

All submitted papers will be thoroughly reviewed to maintain a certain level of quality and standard in order to be considered for ELM2013. Accepted papers need to be presented at the conference. Selected papers will be recommended for further review for publication consideration in special issues of reputable ISI indexed international journals (IEEE Intelligent Systems (see IEEE IS CFP), Neurocomputing, Cognitive Computation, Neural Computing and Applications). Other accepted papers will be published in special edited ELM2013 conference Proceedings volumes by Springer-Verlag. Such submissions should not have been submitted elsewhere and they are not currently under review by other conferences or journals.

  • Paper submission deadline: May 15, 2013 June 10, 2013
  • Notification of acceptance: July 1, 2013 August 1, 2013
  • Registration deadline: August 15, 2013 September 1, 2013


Memetic Computing’s goals are:

  1. 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.

  2. 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.

  3. 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. The first issue is scheduled for January 2009 while accepted papers are expected to appear online via SpringLINK around September 2008.

Aims and scope

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