A Multitasks Learning Approach to Autonomous Agent based on Genetic Network Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{Yang:2012:CEC,
-
title = "A Multitasks Learning Approach to Autonomous Agent
based on Genetic Network Programming",
-
author = "Yang Yang and Shingo Mabu and Kotaro Hirasawa",
-
pages = "1937--1943",
-
booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary
Computation",
-
year = "2012",
-
editor = "Xiaodong Li",
-
month = "10-15 " # jun,
-
DOI = "doi:10.1109/CEC.2012.6256457",
-
address = "Brisbane, Australia",
-
ISBN = "0-7803-8515-2",
-
keywords = "genetic algorithms, genetic programming, Genetic
Network Programming, Evolutionary programming, Parallel
and distributed algorithms",
-
abstract = "The standard methodology in machine learning is to
learn one problem at a time. But, many real-world
problems are complex and have multitasks, and it is a
bit hard to learn them well by one machine learning
approach. So, the simultaneous learning of several
tasks has been considered, that is, so-called multitask
learning. This paper describes a new approach to the
autonomous agent problem using the multitask learning
scheme based on Genetic Network Programming (GNP),
called ML-GNP, where each GNP is used to learn one
corresponding task. MLGNP has some characteristics,
such as distribution, interaction and autonomy, which
are helpful for learning multitask problems. The
experimental results illustrate that ML-GNP can give
much better performance than learning all the tasks of
the problem by one GNP algorithm.",
-
notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
EPS and the IET.",
- }
Genetic Programming entries for
Yang Yang
Shingo Mabu
Kotaro Hirasawa
Citations