Memetic Computing (MC) 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. MC first emerged as population-based meta-heuristic algorithms or hybrid global-local search or more commonly now as memetic algorithm (MA), 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 MAs thus rendering a methodology that balances well generality and problem-specificity.
Taking advantage of both biological selection and cultural selection, a plethora of potentially rich MC methodologies, frameworks and operational meme-inspired algorithms have been developed with considerable success in several real-world domains. Yet, there remain many open issues and opportunities that are continually emerging as intriguing challenges for the field. The primary target of this 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.
