Building a genetically engineerable evolvable program (GEEP) using breadth-based explicit knowledge for predicting software defects

There has been extensive research in the area of data mining over the last decade, but relatively little research in algorithmic mining. Some researchers shun the idea of incorporating explicit knowledge with a Genetic Program environment. At best, very domain specific knowledge is hard wired into the GP modeling process. This work proposes a new approach called the Genetically Engineerable Evolvable Program (GEEP). In this approach, explicit knowledge is made available to the GP. It is considered breadth-based, in that all pieces of knowledge are independent of each other. Several experiments are performed on a NASA-based data set using established equations from other researchers in order to predict software defects. All results are statistically validated.