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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | White, S.K. Martinez, T. Rudolph, G. |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Mathematics and Computer Science, The Citadel, Charleston, South Carolina, USA 29409 (Rudolph, G.) || 27921 NE 152nd St. Duvall, Washington, USA, 98019 (White, S.K.) || Computer Science Department, Brigham Young University, Provo, Utah, USA 84602 (Martinez, T.) |
| Abstract | Reinforcement Programming (RP) is a new approach to automatically generating algorithms, that uses reinforcement learning techniques. This paper describes the RP approach and gives results of experiments using RP to generate a generalized, in-place, iterative sort algorithm. The RP approach improves on earlier results that that use genetic programming (GP). The resulting algorithm is a novel algorithm that is more efficient than comparable sorting routines. RP learns the sort in fewer iterations than GP and with fewer resources. Results establish interesting empirical bounds on learning the sort algorithm: A list of size 4 is sufficient to learn the generalized sort algorithm. The training set only requires one element and learning took less than 200,000 iterations. RP has also been used to generate three binary addition algorithms: a full adder, a binary incrementer, and a binary adder. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 736527 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469093 |
| e-ISBN | 9781424469116 |
| DOI | 10.1109/CEC.2010.5586457 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-07-18 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Sorting Heuristic algorithms Complexity theory Iterative algorithm Arrays Adders |
| Content Type | Text |
| Resource Type | Article |
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