Extending the Scalability of Linkage Learning Genetic Algorithms: Theory and Practice

Chen, Ying-Ping

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https://hdl.handle.net/2142/10799 Copy

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Title

Extending the Scalability of Linkage Learning Genetic Algorithms: Theory and Practice

Author(s)

Chen, Ying-Ping

Issue Date

2004-04

Keyword(s)

Algorithms

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 explanatory 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 understanding 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 subchromosome 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. The study finds that using promoters on the chromosome can improve nucleation potential and promote correct building-block formation. It also observes that the linkage learning genetic algorithm has a consistent, sequential behavior instead of different behaviors on different problems as was previously believed. Moreover, the competition among building blocks of equal salience is the main cause of the exponential growth of convergence time. Finally, adopting subchromosome representations can reduce the competition among building blocks, and therefore, scalable genetic linkage learning for a unimetric approach is possible. Filed as IlliGAL Technical Report No. 2004018 in the Department of General Engineering. Available at PDF version: ftp://ftp-illigal.ge.uiuc.edu/pub/papers/IlliGALs/2004018.pdf Postscript version: ftp://ftp-illigal.ge.uiuc.edu/pub/papers/IlliGALs/2004018.ps.Z

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text

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http://hdl.handle.net/2142/10799

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