Genetic algorithms (GAs) are powerful search techniques based on
principles of evolution and widely applied to solve problems in
many disciplines. However, most GAs employed in practice nowadays
are unable to learn genetic linkage and suffer from the linkage
problem. The linkage learning genetic algorithm (LLGA) was proposed
to tackle the linkage problem with several specially designed
mechanisms. While the LLGA performs much better on badly scaled
problems than simple GAs, it does not work well on uniformly scaled
problems as other competent GAs. Therefore, we need to understand
why it is so and need to know how to design a better LLGA or
whether there are certain limits of such a linkage learning
process. This book aims to gain better understanding of the LLGA in
theory and to improve the LLGA's performance in practice. It starts
with a survey of the existing genetic linkage learning techniques
and describes the steps and approaches taken to tackle the research
topics, including using promoters, developing the convergence time
model, and adopting subchromosomes.
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