| Author | Wan, Hua |
| Call Number | AIT Thesis no.CS-93-30 |
| Subject(s) | Genetic algorithms
|
| Note | A thesis submitted in partial fulfillment of the requirement for the degree of Master of
Science |
| Publisher | Asian Institute of Technology |
| Abstract | To build up a knowledge-based system in Artificial Intelligence (Al), selecting an
appropriate set of rules is one of the key problems.
In this thesis, a Genetic Classifier System approach is employed to optimize the
rules for knowledge-based system. The performance of a classifier system which belongs
to the genetics-based machine learning architecture is tested using the various Genetic
Algorithm operators, namely: reproduction, crossover, and mutation. Together with these
GA operators, rule/message and apportionment of credit determine the fittest set of string
rules, expressed as classifiers which serve as solution set, in two cases, namely: the eleven-multiplexer
task and the Expert System of stock-cutting problem.
Based on the experimental results, it is suggested that Genetic Classifier System is
a feasible approach to optimize the rules for improving the performance of knowledge-based
system. |
| Year | 1993 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Yulu, Qi; |
| Examination Committee(s) | Murai, Shunji;Huynh, Ngoc Phien; |
| Scholarship Donor(s) | Canadian International Development Agency
(CIDA); |
| Degree | Thesis (M.Sc.) - Asian Institute of Technology, 1993 |