EvoDebates: Fit for the future - Applying the algorithm
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"How well can EC adapt to the cluster/grid environment?"
"This is the key technology for dealing with data."
"GAs are quite well developed and are being accepted by industry as a viable design tool."
"I see a very promising line of research in EAs that calibrate themselves online, setting their own parameters for a given problem, while solving that problem."
"In my own communities I have advocated the use of EC on problems which, hitherto, were not susceptible to solution (or effective solution) by traditional approaches. This is by far the most exciting side of EC."
"A very promising area for EC is the manipulation of physical entities, such as robotics, electronics, molecules or bacteria."
"The most profitable development of new technology is in the development of tools dedicated to solving a class of problems such as time tabling."
"One of the areas I consider most promising for evolutionary algorithm research is decision support in general, and multi-objective optimisation in particular."
"We need to develop algorithms that can tackle the raw problems head on."
"A lot of people stress the importance of developing a theory of evolutionary computing. I would suggest, rather, that we use EC to expand our theoretical knowledge of particular problem areas."
"Evolutionary computing techniques need to be seen as optimal information gatherers rather than optimisation tools."
"The sort of problems appropriate for EAs are those whose fitness evaluation function is "difficult": computationally expensive, approximate in character or even largely subjective, affected by noise, having multiple criteria, dynamically varying etc."
"Evolutionary algorithms can be much more that yet another optimization method; they can provide a route to artificial creativity."
"'I asked the computer, and it gave me what seems like a pretty good answer' is a statement that sounds far less ridiculous and disappointing than it once might have."