Emergent Technologies Task Force on


Memetic Computing

 

 

Chair
Yew Soon Ong

Computer Engineering, Nanyang Technological University, Singapore

Members
 

Maoguo Gong
Institute of Intelligent Information Processing, Xidian University, China

 

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

 

Ferrante Neri
University of Jyväskylä, Finland

 

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

 

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

 

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

 

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.

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.

Events Organized By Technical Members


Note: The use of all or part of the materials for any purpose other than personal use, such as lecture handouts, is allowed but should be properly acknowledged.

Last update in September, 2010.