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Author Neri, Filippo
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2002
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Computer programming, programs & data
Subject Keyword Distributed genetic algorithm ♦ First order logic concept learning ♦ Relational concept learing
Abstract Concept learning is a computationally demanding task that involves searching large hypothesis spaces containing candidate descriptions. Stochastic search combined with parallel processing provide a promising approach to successfully deal with such computationally intensive tasks.Learning systems based on distributed genetic algorithms (GA) were able to find concept descriptions as accurate as the ones found by state-of-the-art learning systems based on alternative approaches. However, genetic algorithms' exploitation has the drawback of being computationally demanding.We show how a suitable architectural choice, named cooperative evolution, allows to solve complex applications in an acceptable user waiting time and with a reasonable computational cost by using GA-based learning systems because of the effective exploitation of distributed computation. A variety of experimental settings is analyzed and an explanation for the empirical observations is proposed.
ISSN 10846654
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2002-12-01
Publisher Place New York
e-ISSN 10846654
Journal Journal of Experimental Algorithmics (JEA)
Volume Number 7
Starting Page 12


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Source: ACM Digital Library