An Efficient Ant Colony Programming Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @InProceedings{Li:2018:SmartWorld,
-
author = "Dongrui Li and Yongliang Chen",
-
booktitle = "2018 IEEE SmartWorld, Ubiquitous Intelligence
Computing, Advanced Trusted Computing, Scalable
Computing Communications, Cloud Big Data Computing,
Internet of People and Smart City Innovation
(SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)",
-
title = "An Efficient Ant Colony Programming Approach",
-
year = "2018",
-
pages = "1438--1443",
-
abstract = "In this paper, a novel ant colony optimisation for
linear imperative programming (ACOP) is proposed to
improve the efficiency and accuracy of automating the
design of computer programs. Different from existing
linear genetic programming (LGP), the evolution of ACOP
is based on cooperation of artificial ants. In ACOP,
each solution is a sequence of instructions. The ants
treat elements (registers or operators) in instructions
as nodes in the construction graph. An ant chooses its
next element according to the amount of pheromone
deposited during the generation of a solution. The
performance of ACOP is tested on twelve benchmark
symbolic regression problems. Experimental results show
that ACOP can perform better or competitive in
comparison with two well-known genetic programming
variants.",
-
keywords = "genetic algorithms, genetic programming",
-
DOI = "doi:10.1109/SmartWorld.2018.00249",
-
month = oct,
-
notes = "South China University of Technology Also known as
\cite{8560227}",
- }
Genetic Programming entries for
Dongrui Li
Yongliang Chen
Citations