Description: Genetic algorithms (GAs) have recently been accepted as powerful approaches to
solving optimization problems. It is also well-accepted that building block construction
(schemata formation and conservation) has a positive influence on GA behavior.
Schemata are usually indirectly evaluated through a derived structure. We introduce
a new approach called the Constructive Genetic Algorithm (CGA), which allows
for schemata evaluation and the provision of other new features to the GA. Problems
are modeled as bi-objective optimization problems that consider the evaluation of two
fitness functions. This double fitness process, called fg-fitness, evaluates schemata and
structures in a common basis. Evolution is conducted considering an adaptive rejection
threshold that contemplates both objectives and attributes a rank to each individual in
population. The population is dynamic in size
- [Imageclass] - It`s a class defined by yourself,which o
File list (Check if you may need any files):
GA_for_clustering.PDF
Download