Emergent Technologies Task Force on


Memetic Computing

 

 

Chair
 Yew Soon Ong

Computer Engineering, Nanyang Technological University, Singapore

Members
 Hisao Ishibuchi

 Osaka Prefecture University, Japan

 

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

 

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

 

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

 

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

 

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. Memetic Computing first emerged as population-based meta-heuristic algorithms 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.

In summary, Memetic Computing covers the general aspects of population-based problem-solving methods that are enhanced with some form of cultural-analog mechanism. For instance, Memetic Computing involves also software ecology. That is, studies of the enormous number of software projects are shedding light on how software development takes place and the many social and technical issues related to this fundamental XXI century activity. Related emerging trends that also fall squarely within the remit of Memetic Computing are search based software engineering including the very latest trends on software self-healing, self-assembly and self-management. Memetic Computing is thus an emergent discipline that seeks to distil principles derived both from nature and human societies (i.e. memes and self-organizing mechanisms) as to bring forth the creation of so called Living Technology. The main areas of scientific interest covered by Living Technology, and upon which Memetic Computing would have in near future a definite impact, is the interface between nano-bio-technology and information technology with the ultimate aim of creating new, novel production systems with the properties of self-organization, self-assembly, evolution, learning and, more generally, adaptive complexity. It is also essential to remark that Memetic Computing is also having an impact far beyond technical systems. For example, policy makers and businesses are using memetic strategies (previously called viral marketing) to influence public opinion and deliver effective change at a massive scale through the harnessing and leveraging of memetic concepts operating from small, perhaps unnoticeable, interactions. That is, Memetic computing is playing a key role in the design of bottom-up strategies for the achievement of large societal and technological changes. In this scenario, memetic simulations play a key role in the modeling of strategies and their potential outcomes.

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 October, 2009.