Illinois Genetic Algorithms Laboratory

Chen, Y.-p. (2004)
TR No.: 2004018 | Download PDF | Download PS

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.