Linkage Identification by NFE Estimation: A Practical View of Building Blocks
Date Issued
2011
Date
2011
Author(s)
Fan, Kai-Chun
Abstract
Competent genetic algorithms (competent GAs) identify linkages between genes and build models via various mechanisms to solve problems. They have been applied
for real world applications, but whether the models given by them match what are really preferred to solve the problems is yet unknown. This thesis proposes using the number of function evaluation (Nfe) to measure the performance of models and defines the optimal model to be the one that consumes the fewest Nfe for GAs to solve a specific problem. Then the building blocks (BBs) that construct the optimal model are defined as the correct BBs, and correct linkages exist between any two genes which locate in the same BB. The capabilities of existing linkage-identification metrics, including non-linearity, entropy, simultaneity and DMC, are compared based on this definition. We find that all these metrics fail to identify
correct linkages for some typical problems intrinsically. This thesis then proposes a new metric, named eNFE, directly based on the idea of Nfe to enhance the existing
linkage-identification metrics. Experiment results show that eNFE is able to identify correct linkages for examined problems. The preliminary success suggests another view on learning linkage.
Subjects
Genetic Algorithm
Linkage Learning
Building Block
Convergence Time
Population
Type
thesis
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