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 Dawkinsâ€™ 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.
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