Memetic Computing

 
 

Chair


Ferrante Neri

University of Jyväskylä, Finland


Vice Chairs

 

Maoguo Gong

Institute of Intelligent Information Processing, Xidian University, China

 

Zexuan Zhu

College of Computer Science and Software Engineering, Shenzhen University, China

 

Founding Chair


Yew Soon Ong

Computer Engineering, Nanyang Technological University, Singapore


Members  


Tang Ke

Nature Inspired Computation and Applications Laboratory, School of Computer Science and Technology

University of Science and Technology of China, China

 

Donald C. Wunsch

M.K. Finley Missouri Distinguished Professor, Electrical & Computer Engineering, University of Missouri Rolla, USA

 

Ying-ping Chen

National Chiao Tung University, Taiwan

 

Meng-Hiot Lim

Electrical & Electronics Engineering, Nanyang Technological University, Singapore

 

Licheng Jiao

Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, China

 

Natalio Krasnogor

University of Nottingham, United Kingdom

 

Steven Gustafson

GE Global Research, USA

 

Kay Chen Tan

National University of Singapore, Singapore

 

Yaochu Jin

Honda Research Institute Europe, Germany


Chuan-Kang Ting

National Chung Cheng University, Taiwan

 

Jim Smith

University of the West of England

 

Ruhul Sarker

The University of New South Wales

 

Shaheen Fatima

Loughborough University, United Kingdom

 

Goh, Chi Keong

Advanced Technology Centre, Rolls-Royce Singapore Pte. Ltd, Singapore

 

Swagatam Das

Department of Electronics and Telecommunication Engineering, Jadavpur University

 

Lee Kee Khoon, Gary

Institute of High Performance Computing, A-Star, Singapore

 

Yanqing Zhang

Department of Computer Science, Georgia State University, Atlanta, Georgia, USA

 

Pablo Moscato

School of Electrical Engineering and Computer Science The University of Newcastle, Australia

 

Carlos Cotta

Universidad de Málaga, ETSI Informática, Campus de Teatinos, Spain

 

Anna Kononova

Heriot-Watt University, UK

 

Ernesto Mininno

Department of Mathematical Information Technology, University of Jyväskylä, Finland


Bo Liu

Center for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences

Task Force Members

DEFINITION:

Memetic Computing is a broad subject which studies complex and dynamic computing structures composed of interacting modules (memes) whose evolution dynamics is inspired by the diffusion of ideas.

Memes are simple strategies whose harmonic coordination allows the solution of various problems.

 

Background

The use of sophisticated computational intelligence approaches for solving complex problems in science and engineering has increased steadily over the last 20 years. Within this growing trend, which relies heavily on state-of-the-art optimisation and design strategies, the methodology known as Memetic Computing is, perhaps, one of the recent most successful stories.

From the word "mimeme" of Greek origin, Dawkins coined the term "meme" in his 1976 book on "The Selfish Gene" (Dawkins 1976). He defined it as being "the basic unit of cultural transmission or imitation". These days, the monosyllabic word "meme" that is an analog of the word "gene" has since taken flight to become one of the most successful metaphorical ideologies in computational intelligence. The new science of memetics today represents the mind-universe analog to genetics in cultural evolution, stretching across the fields of anthropology, biology, cognition, psychology, sociology and sociobiology.

Today, we are in an era where a plethora of computational problem-solving methodologies are being invented to tackle the diverse problems that are of interest to researchers. Some of these problems have emerged from real-life scenarios while some are theoretically motivated and created to stretch the bounds of current computational algorithms. Regardless, it is clear that in this new millennium a unifying concept to dissolve the barriers among these techniques will help to advance the course of algorithmic research. Interestingly, there is a parallel that can be drawn in memes from both socio-cultural and computational perspectives. The platform for memes in the former is the human minds while in the latter, the platform for memes is algorithms for problem-solving. In this context, memes can culminate into representations that enhance the problem-solving capability of algorithms.

The phrase Memetic Computing has surfaced in recent years; emerging as a discipline of research that focuses on the use of memes as units of information which is analogous to memes in a social and cultural context. Memetic Computing has first emerged as population-based meta-heuristic algorithms or hybrid global-local search or more commonly now as memetic algorithm that are inspired by Darwinian principles of natural selection and Dawkins’ notion of a meme defined as a unit of cultural evolution that is capable of local/individual refinements. The metaphorical parallels to, on the one hand, Darwinian evolution and, on the other hand, between memes and domain specific heuristics are captured within memetic algorithms thus rendering a methodology that balances well generality and problem-specificity. Hence Memetic Computing captures the power of both biological selection and cultural selection. The idea of going beyond biological evolution towards a dual track comprising biological-cultural selection has indeed transcended the field of combinatorial and continuous optimization. Most importantly, recent research work has also shown that the concept of "meme" dispersal and selection can be exploited in, for example, robotics engineering, multi-agent systems, robotics, optimization, software engineering, and the social sciences.

The term Memetic Computing is often unassumingly taken to mean the same thing as memetic algorithms in a synonymous manner. Clearly, such a narrow and restrictive notion or perception of Memetic computing does not do justice to the expanse of this research discipline. Memetic computing thus offers a much broader scope, perpetuating the idea of memes into concepts that capture the richness of algorithms that defines a new generation of computational methodologies. It is defined as a paradigm that uses the notion of meme(s) as units of information encoded in computational representations for the purpose of problem solving.

Target and Motivation

The primary target of the task Force is to promote research on Memetic Computing. Further the task force aims at bringing researchers from academia and industry together to explore future directions of research and to publicize the new and emerging concept of memetics in computational intelligence to a wider audience. Specifically, we seek for diverse state-of-the-art concepts, theory, and practice of memetic computation that are close to evolutionary principles.

  1. Novel concepts of memetic computation and its adaptation into evolutionary framework and algorithms

  2. Competitive, collaborative and cooperative agent based memetic computation

  3. Cognitive & Brain inspired memetic computation

  4. Meme-gene coevolutionary frameworks and multi-inheritance model

  5. Formal and Probabilistic Single/Multi-Objective memetic frameworks

  6. Analytical/Theoretical advances in memetic framework

  7. Memes, memeplexes, meta-memes in computing and high-order evolution

  8. Memetic frameworks that mimics individual learning, social learning and imitation

  9. Partial or full or meta-Lamarckian/Baldwinian, meta-learning, agent based memetic computation

  10. Parallel Memetic framework

  11. Memetic frameworks for handling computationally expensive problems
     

Events Organized By Technical Members

  1. Wikipedia like Knowledge Web on Memetic Computation

  2. 2011 IEEE Workshop on Memetic Computing, IEEE Symposium Series on Computational Intelligence 2011, April 11-15, 2011 - Paris, France. Organizers: Zexuan Zhu, Maoguo Gong, Zhen Ji & Yew-Soon Ong.

  3. Special Issue on Engineering Applications of Memetic Computing, IEEE Transactions on Systems, Man and Cybernetics Part C - Applications & Reviews, Submission deadline: December 31, 2010.  

  4. Special Issue on Advances in Memetic Computation,  IEEE Transactions on Evolutionary Computation, Organizers: Yew-Soon Ong, Kay Chen Tan.  

  5. Special Session on 'From Hybrid Evolutionary Computation and Hyper-Heuristics to Memetic Computation', IEEE World Congress on Computational Intelligence,  WCCI 2010, CEC 2010, Barcelona, Spain, 18-23 July 2010, Organizers: Gabriela Ochoa, Shaheen Fatima, Ferrante Neri and Yew-Soon Ong.

  6. Special Session on Agent Based Memetic Algorithms, IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway, 18-21 May, 2009 Organizers: Ruhul Sarker, Michela Milano & Andrea Roli  

  7. Special Session on Memetic Algorithms for Hard to Solve Problems, IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway, 18-21 May, 2009, Organizer: Ferrante Neri, Pablo Moscato & Hisao Ishibuchi

  8. Special Issue on 'Emerging Trends in Soft Computing - Memetic Algorithm', Soft Computing Journal, In Press.

  9. Special Session on Memetic Algorithms, IEEE World Congress on Computational Intelligence, WCCI 2008, CEC, Hong Kong, Organizers: Yew-Soon Ong, Ferrante Neri, Hisao Ishibuchi and Meng Hiot Lim.

  10. 'Memetic Computing' by Thomson Scientific's Essential Science Indicators as an Emerging Front.

  11. Special Session on Memetic Algorithms, IEEE Congress on Evolutionary Computation, CEC 2007, Singapore, Organizers: Yew-Soon Ong, Ferrante Neri, Hisao Ishibuchi and Meng Hiot Lim.

  12. Special Issue on Memetic Algorithms, IEEE Transactions on Systems, Man and Cybernetics - Part B, Vol. 37, No. 1, February 2007.

  13. Recent Advances in Memetic Algorithms, Series: Studies in Fuzziness and Soft Computing , Vol. 166, ISBN: 978-3-540-22904-9, 2005.


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