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AUTOMATIC ALGORITHM DEVELOPMENT USING NEW REINFORCEMENT PROGRAMMING TECHNIQUES
Author(s) -
White Spencer,
Martinez Tony,
Rudolph George
Publication year - 2012
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2012.00413.x
Subject(s) - sort , computer science , sorting algorithm , sorting , algorithm , reinforcement learning , binary number , set (abstract data type) , artificial intelligence , mathematics , arithmetic , programming language , information retrieval
Reinforcement Programming (RP) is a new approach to automatically generating algorithms that uses reinforcement learning techniques. This paper introduces the RP approach and demonstrates its use to generate a generalized, in‐place, iterative sort algorithm. The RP approach improves on earlier results 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. Experiments 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. Additionally RP was used to generate three binary addition algorithms: a full adder, a binary incrementer, and a binary adder.