Semantically-based crossover in genetic programming : application to real-valued symbolic regression

Files in This Item:
File Description SizeFormat 
uy_gpem.pdf254.69 kBAdobe PDFDownload
Title: Semantically-based crossover in genetic programming : application to real-valued symbolic regression
Authors: Nguyen, Quang Uy
Nguyen, Xuan Hoai
O'Neill, Michael
McKay, Bob (Bob I.)
Galván-López, Edgar
Permanent link: http://hdl.handle.net/10197/3528
Date: Jun-2011
Abstract: We investigate the effects of semantically-based crossover operators in Genetic Programming, applied to real-valued symbolic regression problems. We propose two new relations derived from the semantic distance between subtrees, known as Semantic Equivalence and Semantic Similarity. These relations are used to guide variants of the crossover operator, resulting in two new crossover operators – Semantics Aware Crossover (SAC) and Semantic Similarity-based Crossover (SSC). SAC, was introduced and previously studied, is added here for the purpose of comparison and analysis. SSC extends SAC by more closely controlling the semantic distance between subtrees to which crossover may be applied. The new operators were tested on some real-valued symbolic regression problems and compared with Standard Crossover (SC), Context Aware Crossover (CAC), Soft Brood Selection (SBS), and No Same Mate (NSM) selection. The experimental results show on the problems examined that, with computational effort measured by the number of function node evaluations, only SSC and SBS were significantly better than SC, and SSC was often better than SBS. Further experiments were also conducted to analyse the perfomance sensitivity to the parameter settings for SSC. This analysis leads to a conclusion that SSC is more constructive and has higher locality than SAC, NSM and SC; we believe these are the main reasons for the improved performance of SSC.
Funding Details: Irish Research Council for Science, Engineering and Technology
Type of material: Journal Article
Publisher: Springer
Copyright (published version): 2010 Springer Science+Business Media
Keywords: Genetic programming;Semantics;Crossover;Symbolic regression;Locality
Subject LCSH: Genetic programming (Computer science)
Semantic computing
DOI: 10.1007/s10710-010-9121-2
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection
CASL Research Collection

Show full item record

SCOPUSTM   
Citations 1

105
Last Week
1
Last month
checked on Jun 22, 2018

Page view(s) 50

107
checked on May 25, 2018

Download(s) 10

1,022
checked on May 25, 2018

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.