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Extending the Scalability of Linkage Learning Genetic Algorithms: Theory and Practice (2004)
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BibTeX
@TECHREPORT{Chen04extendingthe,
author = {Ying-ping Chen},
title = {Extending the Scalability of Linkage Learning Genetic Algorithms: Theory and Practice},
institution = {},
year = {2004}
}
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Abstract
There are two primary objectives of this dissertation. The first goal is to identify certain limits of genetic algorithms that use only fitness for learning genetic linkage. Both an ex-planatory theory and experimental results to support the theory are provided. The other goal is to propose a better design of the linkage learning genetic algorithm. After under-standing the cause of the performance barrier, the design of the linkage learning genetic algorithm is modified accordingly to improve its performance on uniformly scaled problems. This dissertation starts with presenting the background of the linkage learning genetic algorithm. Then, it introduces the use of promoters on the chromosome to improve the performance of the linkage learning genetic algorithm on uniformly scaled problems. The convergence time model is constructed by identifying the sequential behavior, developing the tightness time model, and establishing the connection in between. The use of subchro-mosome representations is to avoid the limit implied by the convergence time model. The experimental results demonstrate that the use of subchromosome representations may be a promising way to design a better linkage learning genetic algorithm.
Keyphrases
genetic algorithm linkage learning genetic algorithm convergence time model experimental result sequential behavior promising way tightness time model first goal performance barrier subchromosome representation certain limit subchro-mosome representation ex-planatory theory primary objective genetic linkage
