Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7661
Title: Balancing samples’ contributions on GA learning
Authors: Prof. LEUNG Kwong Sak 
Lee, Kin Hong 
Cheang, Sin Man 
Issue Date: 2001
Publisher: Springer Verlag
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2001, vol. 2210, pp. 256 - 266
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 
Abstract: A main branch in Evolutionary Computation is learning a system directly from input/output samples without investigating internal behaviors of the system. Input/output samples captured from a real system are usually incomplete, biased and noisy. In order to evolve a precise system, the sample set should include a complete set of samples. Thus, a large number of samples should be used. Fitness functions being used in Evolutionary Algorithms usually based on the matched ratio of samples. Unfortunately, some of these samples may be exactly or semantically duplicated. These duplicated samples cannot be identified simply because we do not know the internal behavior of the system being evolved. This paper proposes a method to overcome this problem by using a dynamic fitness function that incorporates the contribution of each sample in the evolutionary process. Experiments on evolving Finite State Machines with Genetic Algorithms are presented to demonstrate the effect on improving the successful rate and convergent speed of the proposed method. © Springer-VerlagBerlin Heidelberg 2001.
Type: Conference Paper
URI: http://hdl.handle.net/20.500.11861/7661
ISBN: 354042671X
978-354042671-4
ISSN: 03029743
DOI: 10.1007/3-540-45443-8_23
Appears in Collections:Applied Data Science - Publication

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