Comparison between Lamarckian Evolution and Baldwin Evolution of neural network

Genetic Algorithms are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. Hybrid genetic algorithms are the combination of learning algorithms(Back propagation), usually...

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Main Authors:	Taha, Imad, Inazy, Qabas
Format:	Article
Language:	English
Published:	Al Rafidain University College 2006
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/6683/ http://irep.iium.edu.my/6683/ http://irep.iium.edu.my/6683/1/imad-qabas.pdf

id	iium-6683
recordtype	eprints
spelling	iium-66832013-07-31T04:51:03Z http://irep.iium.edu.my/6683/ Comparison between Lamarckian Evolution and Baldwin Evolution of neural network Taha, Imad Inazy, Qabas QA75 Electronic computers. Computer science Genetic Algorithms are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. Hybrid genetic algorithms are the combination of learning algorithms(Back propagation), usually working as evaluation functions, and genetic algorithms. There are two basic strategies in using hybrid GAs, Lamarckian and Baldwinian evolution. Traditional schema theory does not support Lamatckian learning, i.e, forcing the genetic representation to match the solution found by the learning algorithm. However, Lamarckian learning does alleviate the problem of multiple genotypes mapping to the same phenotype. Baldwinian learning uses learning algorithm to change the fitness landscape, but the solution that is found is not encoded back into genetic string. We presented hybrid genetic algorithm for optimizing weights as well as the topology of artificial neural networks, by introducing the concepts of Lamarckian and Baldwin evolution effects. Experimental results with extensive set of experiments show that the hybrid GA exploiting the Baldwin effect more effect than Lamarckian evolution but is slow in convergence, and the results of proposed algorithms outperform those of previous algorithms. Al Rafidain University College 2006 Article PeerReviewed application/pdf en http://irep.iium.edu.my/6683/1/imad-qabas.pdf Taha, Imad and Inazy, Qabas (2006) Comparison between Lamarckian Evolution and Baldwin Evolution of neural network. Journal of Al Rafidain University College, 8 (19). pp. 217-232. ISSN 1681-6870 http://www.coalrafidain.edu.iq
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Taha, Imad Inazy, Qabas Comparison between Lamarckian Evolution and Baldwin Evolution of neural network
description	Genetic Algorithms are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. Hybrid genetic algorithms are the combination of learning algorithms(Back propagation), usually working as evaluation functions, and genetic algorithms. There are two basic strategies in using hybrid GAs, Lamarckian and Baldwinian evolution. Traditional schema theory does not support Lamatckian learning, i.e, forcing the genetic representation to match the solution found by the learning algorithm. However, Lamarckian learning does alleviate the problem of multiple genotypes mapping to the same phenotype. Baldwinian learning uses learning algorithm to change the fitness landscape, but the solution that is found is not encoded back into genetic string. We presented hybrid genetic algorithm for optimizing weights as well as the topology of artificial neural networks, by introducing the concepts of Lamarckian and Baldwin evolution effects. Experimental results with extensive set of experiments show that the hybrid GA exploiting the Baldwin effect more effect than Lamarckian evolution but is slow in convergence, and the results of proposed algorithms outperform those of previous algorithms.
format	Article
author	Taha, Imad Inazy, Qabas
author_facet	Taha, Imad Inazy, Qabas
author_sort	Taha, Imad
title	Comparison between Lamarckian Evolution and Baldwin Evolution of neural network
title_short	Comparison between Lamarckian Evolution and Baldwin Evolution of neural network
title_full	Comparison between Lamarckian Evolution and Baldwin Evolution of neural network
title_fullStr	Comparison between Lamarckian Evolution and Baldwin Evolution of neural network
title_full_unstemmed	Comparison between Lamarckian Evolution and Baldwin Evolution of neural network
title_sort	comparison between lamarckian evolution and baldwin evolution of neural network
publisher	Al Rafidain University College
publishDate	2006
url	http://irep.iium.edu.my/6683/ http://irep.iium.edu.my/6683/ http://irep.iium.edu.my/6683/1/imad-qabas.pdf
first_indexed	2023-09-18T20:15:47Z
last_indexed	2023-09-18T20:15:47Z
_version_	1777407777700839424

Comparison between Lamarckian Evolution and Baldwin Evolution of neural network

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