Classifier Systems for Continuous Payoff Environments
نویسنده
چکیده
Recognizing that many payoff functions are continuous and depend on the input state x, the classifier system architecture XCS is extended so that a classifier’s prediction is a linear function of x. On a continuous nonlinear problem, the extended system, XCS-LP, exhibits high performance and low error, as well as dramatically smaller evolved populations compared with XCS. Linear predictions are seen as a new direction in the quest for powerful generalization in classifier systems.
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تاریخ انتشار 2004