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https://hdl.handle.net/2440/108286
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Type: | Conference paper |
Title: | Boosting search for recursive functions using partial call-trees |
Author: | Alexander, B. Zacher, B. |
Citation: | Lecture Notes in Artificial Intelligence, 2014, pp.384-393 |
Publisher: | Springer, Cham |
Issue Date: | 2014 |
Series/Report no.: | Lecture Notes in Computer Science book series, vol. 8672 |
ISBN: | 978-3-319-10761-5 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | 13th International Conference on Parallel Problem Solving from Nature (PPSN XIII) (13 Sep 2014 - 17 Sep 2014 : Ljubljana, Slovenia) |
Statement of Responsibility: | Brad Alexander and Brad Zacher |
Abstract: | Recursive functions are a compact and expressive way to solve challenging problems in terms of local processing. These properties have made recursive functions a popular target for genetic programming. Unfortunately, the evolution of substantial recursive programs has proven difficult. One cause of this problem is the difficulty in evolving both correct base and recursive cases using just information derived from running test cases. In this work we describe a framework that exploits additional information in the form of partial call-trees. Such trees - a by-product of deriving input-output cases by hand - guides the search process by allowing the separate evolution of the recursive case. We show that the speed of evolution of recursive functions is significantly enhanced by the use of partial call-trees and demonstrate application of the technique in the derivation of functions for a suite of numerical functions. |
Keywords: | Recursion, genetic programming, call-tree, adaptive grammar |
Rights: | © Springer International Publishing Switzerland 2014 |
DOI: | 10.1007/978-3-319-10762-2_38 |
Published version: | http://dx.doi.org/10.1007/978-3-319-10762-2_38 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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