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M9 1a8 8 0 1 0 0 16A8 8 0 0 0 9 1zm. Computer Science Stack Exchange is a question and answer site for students, researchers and practitioners of computer science. Why has research on genetic algorithms slowed? I was told that research has really slowed in this field.
The reason given was that most people are focusing on machine learning and data mining. DM have when compared with GA? As far as I know, if many algorithm are given that can solve a specific problem, GA won’t be the best one in most cases. Well, machine learning in the sense of statistical pattern recognition and data mining are definitely hotter areas, but I wouldn’t say research in evolutionary algorithms has particularly slowed. The two areas aren’t generally applied to the same types of problems.
It’s not immediately clear how a data driven approach helps you, for instance, figure out how to best schedule worker shifts or route packages more efficiently. Evolutionary methods are most often used on hard optimization problems rather than pattern recognition. The most direct competitors are operations research approaches, basically mathematical programming, and other forms of heuristic search like tabu search, simulated annealing, and dozens of other algorithms collectively known as “metaheuristics”. All that said, there are several advantages the field more generally thought of as “machine learning” has in any comparison of “hotness”. One, it tends to be on much firmer theoretical ground, which the mathematicians always like. Two, we’re in something of a golden age for data, and lots of the cutting edge machine learning methods really only start to shine when given tons of data and tons of compute power, and in both respects, the time is in a sense “right”. I’m not sure what specifically you’d like me to elaborate on.