We ﬁrst present the data settings and describe the corresponding preprocessing procedure brieﬂy. Let
H = {D1, . . . , DNH} be the raw historical data with NH previous records. The i-th record Di is
deﬁned as a three-element tuple (cid:104)Ti, Si, Mi(cid:105) which contains a task Ti, a solution Si ∈ S, and the
evaluated metric performance Mi, e.g., classiﬁcation accuracy.
Note that, the historical data H may have heterogeneous and diverse formats. For example, the task
can be described in natural text, while the solution can be a JSON conﬁguration, a row of tabular data,
or a code snippet. An experience pool PE is constructed, to canonicalize the data and store them as
experiences PE = {E1, . . . , ENE}, where Ei = C(Di), and C(·) is the canonicalization function.
For the simplicity of notations, we assume that all the solutions within PE come from a universal
solution space, without loss of generality. It could be easy to extend the framework to scenarios with
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