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