Undirected Training of Run Transferable Libraries
This paper investigates the robustness of Run Transferable Libraries(RTLs) on scaled problems. RTLs provide GP with a library of functions which replace the usual primitive functions provided when approaching a problem. The RTL evolves from run to run using feedback based on function usage, and has been shown to outperform GP by an order of magnitude on a variety of scalable problems.
RTLs can, however, also be applied across a domain of related problems, as well as across a range of scaled instances of a single problem. To do this successfully, it will need to balance a range of functions. We introduce a problem that can deceive the system into converging to a sub-optimal set of functions, and demonstrate that this is a consequence of the greediness of the library update algorithm.
We demonstrate that a much simpler, truly evolutionary, update strategy doesn’t suffer from this problem, and exhibits far better optimization properties than the original strategy.
KeywordsGenetic Program Problem Instance Genetic Program System Multiplexer Problem Library Content
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