11° Dauin Lunch Seminar - What We Talk About When We Talk About Evolutionary Computation
According to Encyclopedia Britannica, natural evolution is the theory postulating that the various types of plants, animals, and other living things on Earth have their origin in other preexisting types and that the distinguishable differences are due to modifications in successive generations. The elegance of the Darwinian "Natural Selection" fascinated generations of researchers since the end of the XIX century, and inspired different computer scientists in the late 1960s. Their algorithms, loosely inspired by natural evolution, have been successfully used in the past decades for exploring new research lines and solving practical, industrial problems. The fact should not be surprising, as the relationship between learning and evolution has been pointed out by Alan Turing back in 1950 in his seminal work "Computing machinery and intelligence". However, despite its success stories, Evolutionary Computation never experienced a windfall such as Machine Learning today, and these techniques remained almost unnoticed by the general public despite being steadily exploited by practitioners and scholars. The talk will briefly sketch the origin of Evolutionary Computation, what the different research lines have in common and where they differ, and it will highlight the common pitfalls. The current status of the field will be described, along with its potential relationships with modern Machine Leaning (the so-called Evolutionary Machine Learning).
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