Knowledge reuse in genetic programming applied to
visual learning
W. Jaskowski, K. Krawiec, and B. Wieloch. GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation, 2, page 1790--1797. London, ACM Press, (7-11 July 2007)
Abstract
We propose a method of knowledge reuse for an ensemble
of genetic programming-based learners solving a visual
learning task. First, we introduce a visual learning
method that uses genetic programming individuals to
represent hypotheses. Individuals-hypotheses process
image representation composed of visual primitives
derived from the training images that contain objects
to be recognised. The process of recognition is
generative, i.e., an individual is supposed to restore
the shape of the processed object by drawing its
reproduction on a separate canvas. This canonical
method is extended with a knowledge reuse mechanism
that allows a learner to import genetic material from
hypotheses that evolved for the other decision classes
(object classes). We compare the performance of the
extended approach to the basic method on a real-world
tasks of handwritten character recognition, and
conclude that knowledge reuse leads to significant
convergence speedup and, more importantly,
significantly reduces the risk of overfitting.
GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation
year
2007
month
7-11 July
pages
1790--1797
publisher
ACM Press
volume
2
organisation
ACM SIGEVO (formerly ISGEC)
publisher_address
New York, NY, USA
isbn13
978-1-59593-697-4
notes
GECCO-2007 A joint meeting of the sixteenth
international conference on genetic algorithms
(ICGA-2007) and the twelfth annual genetic programming
conference (GP-2007).
ACM Order Number 910071
%0 Conference Paper
%1 1277318
%A Jaskowski, Wojciech
%A Krawiec, Krzysztof
%A Wieloch, Bartosz
%B GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation
%C London
%D 2007
%E Thierens, Dirk
%E Beyer, Hans-Georg
%E Bongard, Josh
%E Branke, Jurgen
%E Clark, John Andrew
%E Cliff, Dave
%E Congdon, Clare Bates
%E Deb, Kalyanmoy
%E Doerr, Benjamin
%E Kovacs, Tim
%E Kumar, Sanjeev
%E Miller, Julian F.
%E Moore, Jason
%E Neumann, Frank
%E Pelikan, Martin
%E Poli, Riccardo
%E Sastry, Kumara
%E Stanley, Kenneth Owen
%E Stutzle, Thomas
%E Watson, Richard A
%E Wegener, Ingo
%I ACM Press
%K Genetics-Based Learning, Machine algorithms, genetic knowledge pattern programming, recognition reuse,
%P 1790--1797
%T Knowledge reuse in genetic programming applied to
visual learning
%U http://doi.acm.org/10.1145/1276958.1277318
%V 2
%X We propose a method of knowledge reuse for an ensemble
of genetic programming-based learners solving a visual
learning task. First, we introduce a visual learning
method that uses genetic programming individuals to
represent hypotheses. Individuals-hypotheses process
image representation composed of visual primitives
derived from the training images that contain objects
to be recognised. The process of recognition is
generative, i.e., an individual is supposed to restore
the shape of the processed object by drawing its
reproduction on a separate canvas. This canonical
method is extended with a knowledge reuse mechanism
that allows a learner to import genetic material from
hypotheses that evolved for the other decision classes
(object classes). We compare the performance of the
extended approach to the basic method on a real-world
tasks of handwritten character recognition, and
conclude that knowledge reuse leads to significant
convergence speedup and, more importantly,
significantly reduces the risk of overfitting.
@inproceedings{1277318,
abstract = {We propose a method of knowledge reuse for an ensemble
of genetic programming-based learners solving a visual
learning task. First, we introduce a visual learning
method that uses genetic programming individuals to
represent hypotheses. Individuals-hypotheses process
image representation composed of visual primitives
derived from the training images that contain objects
to be recognised. The process of recognition is
generative, i.e., an individual is supposed to restore
the shape of the processed object by drawing its
reproduction on a separate canvas. This canonical
method is extended with a knowledge reuse mechanism
that allows a learner to import genetic material from
hypotheses that evolved for the other decision classes
(object classes). We compare the performance of the
extended approach to the basic method on a real-world
tasks of handwritten character recognition, and
conclude that knowledge reuse leads to significant
convergence speedup and, more importantly,
significantly reduces the risk of overfitting.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {London},
author = {Jaskowski, Wojciech and Krawiec, Krzysztof and Wieloch, Bartosz},
biburl = {https://www.bibsonomy.org/bibtex/23edae65e561339d977a6f3fca3de5c84/brazovayeye},
booktitle = {GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation},
editor = {Thierens, Dirk and Beyer, Hans-Georg and Bongard, Josh and Branke, Jurgen and Clark, John Andrew and Cliff, Dave and Congdon, Clare Bates and Deb, Kalyanmoy and Doerr, Benjamin and Kovacs, Tim and Kumar, Sanjeev and Miller, Julian F. and Moore, Jason and Neumann, Frank and Pelikan, Martin and Poli, Riccardo and Sastry, Kumara and Stanley, Kenneth Owen and Stutzle, Thomas and Watson, Richard A and Wegener, Ingo},
interhash = {62a0ef43b17107fac9d4deddfa7982b3},
intrahash = {3edae65e561339d977a6f3fca3de5c84},
isbn13 = {978-1-59593-697-4},
keywords = {Genetics-Based Learning, Machine algorithms, genetic knowledge pattern programming, recognition reuse,},
month = {7-11 July},
notes = {GECCO-2007 A joint meeting of the sixteenth
international conference on genetic algorithms
(ICGA-2007) and the twelfth annual genetic programming
conference (GP-2007).
ACM Order Number 910071},
organisation = {ACM SIGEVO (formerly ISGEC)},
pages = {1790--1797},
publisher = {ACM Press},
publisher_address = {New York, NY, USA},
timestamp = {2008-06-19T17:42:24.000+0200},
title = {Knowledge reuse in genetic programming applied to
visual learning},
url = {http://doi.acm.org/10.1145/1276958.1277318},
volume = 2,
year = 2007
}