Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Cross-task code reuse in genetic programming applied to visual learning

100%

Jaśkowski W., Krawiec K., Wieloch B.

International Journal of Applied Mathematics and Computer Science

2014

tom 24

nr 1

183-197

We propose a method that enables effective code reuse between evolutionary runs that solve a set of related visual learning tasks. We start with introducing a visual learning approach that uses genetic programming individuals to recognize objects. The process of recognition is generative, i.e., requires the learner to restore the shape of the processed object. This method is extended with a code reuse mechanism by introducing a crossbreeding operator that allows importing the genetic material from other evolutionary runs. In the experimental part, we compare the performance of the extended approach to the basic method on a real-world task of handwritten character recognition, and conclude that code reuse leads to better results in terms of fitness and recognition accuracy. Detailed analysis of the crossbred genetic material shows also that code reuse is most profitable when the recognized objects exhibit visual similarity.

Ograniczanie wyników

1 International Journal of Applied Mathematics and Computer Science

1 Jaśkowski W.

1 Krawiec K.

1 Wieloch B.

1 2014

Wyniki wyszukiwania

Cross-task code reuse in genetic programming applied to visual learning